diff --git a/CHANGELOG.md b/CHANGELOG.md index 589626453..6857e20fc 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,11 +9,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Added +- [#407](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/407): GCI115 - Python - Detect a for loop and suggest a list comprehension - [#481](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/481) Add rule GCI114 for Python: Avoid range(len()) pattern, prefer direct iteration or enumerate() - Add search words in rules file - -### Added -- [#407](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/407): GCI115 - Python - Detect a for loop and suggest a list comprehension +- [#487](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/487) Add complete analysis for FetchType issue for Java (sonarqube built-in rule S6904) ### Changed diff --git a/RULES.md b/RULES.md index 81b5d17e4..d3b1b8e16 100644 --- a/RULES.md +++ b/RULES.md @@ -20,7 +20,6 @@ Some are applicable for different technologies. | Rule key | Name | Description | Reference/Validation | Java | Php | JS | Python | Rust | C# | HTML | Mobile Android | Mobile iOS | Source reference | |----------|-------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------|-----|----|--------|------|----|------|----------------|------------|--------------------------------------------------------------------------------------------------------------------------| -| CRJVM205 | Force usage of FetchType LAZY for collections on JPA entities | | | 🚧 | ❓ | ❓ | ❓ | ❓ | ❓ | 🚫 | 🚫 | 🚫 | | | CRJVM206 | Avoid N+1 selects problem | | | 🚧 | ❓ | ❓ | ❓ | ❓ | ❓ | 🚫 | 🚫 | 🚫 | | | CRJVM207 | Customer data must have end-of-life information | | | 🚧 | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | 🚫 | 🚫 | | | CRJVM??? | Persistence Java : Avoid Joints on non indexed columns | | | 🚧 | ❓ | ❓ | ❓ | ❓ | ❓ | 🚫 | 🚫 | 🚫 | | @@ -180,18 +179,19 @@ will be completely deleted in next releases and moved to bottom deleted rules ar This table lists rules proposed by the community but refused or/and deleted in creedengo plugins with the justification. -| Rule key | Language | Name | Description | Invalidation | -|----------|------------|-------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| CRDOM203 | HTML | HTML page must contain a doctype tag | The difference in performance is negligible, this rule is more related to the user experience. | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/103) | -| CRPYT | Python | Use numpy array instead of standard list | The use of numpy library to perform array manipulation is more energy efficient than the use of the standard list functions. | [Github discussion with measures](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/132) | -| GCI4 | Java | Avoid using global variables | Global variables do not exist in Java. | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/233) | -| GCI8 | JavaScript | Avoid using high accuracy geolocation | The rule has been merged with the rule GCI523 | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/318) | -| GCI34 | Python | Using try...catch...finally calls | Implementation is too simple (only detection of presence of "try" statement) AND replaced by `GCI35` rule | Github discussion with measures : [general/java](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/128) / [python](https://github.com/green-code-initiative/creedengo-python/pull/6) | -| GCI34 | PHP | Using try...catch...finally calls | Implementation is too simple (only detection of presence of "try" statement) AND replaced by `GCI35` rule | Github discussion with measures : [general/java](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/128) / [php](https://github.com/green-code-initiative/creedengo-php/pull/10) | -| GCI63 | Java | Unnecessarily assigning values to variables | There are already 3 native SonarQube rules for Java. | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/258) | -| GCI66 | Java | Use single quote (') instead of quotation mark (") | Finally, not applicable for Java | Discussed with core team | -| GCI66 | JS | Use single quote (') instead of quotation mark (") | No difference in performance | Discussed with core team | -| GCI66 | Python | Use single quote (') instead of quotation mark (") | Finally, not applicable for Python | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-python/issues/4) | -| GCI66 | PHP | Use single quote (') instead of quotation mark (") | Finally, not applicable for PHP | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-php/issues/58) | -| GCI69 | Python | Calling a loop invariant function in a loop condition | Finally, not applicable for Python | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-python/issues/21) | -| GCI75 | Java | Don't concatenate strings in loops | Optimizations on Java concatenation Strings are useless since JDK8 | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/246) | +| Rule key | Language | Name | Description | Invalidation | +|----------|------------|---------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| CRDOM203 | HTML | HTML page must contain a doctype tag | The difference in performance is negligible, this rule is more related to the user experience. | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/103) | +| CRJVM205 | Java | Force usage of FetchType LAZY for collections on JPA entities | The rule already exists inside SonarQube : built-in rule "java:S6904" | [Github discussion with analysis](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/487) | +| CRPYT | Python | Use numpy array instead of standard list | The use of numpy library to perform array manipulation is more energy efficient than the use of the standard list functions. | [Github discussion with measures](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/132) | +| GCI4 | Java | Avoid using global variables | Global variables do not exist in Java. | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/233) | +| GCI8 | JavaScript | Avoid using high accuracy geolocation | The rule has been merged with the rule GCI523 | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/318) | +| GCI34 | Python | Using try...catch...finally calls | Implementation is too simple (only detection of presence of "try" statement) AND replaced by `GCI35` rule | Github discussion with measures : [general/java](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/128) / [python](https://github.com/green-code-initiative/creedengo-python/pull/6) | +| GCI34 | PHP | Using try...catch...finally calls | Implementation is too simple (only detection of presence of "try" statement) AND replaced by `GCI35` rule | Github discussion with measures : [general/java](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/128) / [php](https://github.com/green-code-initiative/creedengo-php/pull/10) | +| GCI63 | Java | Unnecessarily assigning values to variables | There are already 3 native SonarQube rules for Java. | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/258) | +| GCI66 | Java | Use single quote (') instead of quotation mark (") | Finally, not applicable for Java | Discussed with core team | +| GCI66 | JS | Use single quote (') instead of quotation mark (") | No difference in performance | Discussed with core team | +| GCI66 | Python | Use single quote (') instead of quotation mark (") | Finally, not applicable for Python | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-python/issues/4) | +| GCI66 | PHP | Use single quote (') instead of quotation mark (") | Finally, not applicable for PHP | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-php/issues/58) | +| GCI69 | Python | Calling a loop invariant function in a loop condition | Finally, not applicable for Python | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-python/issues/21) | +| GCI75 | Java | Don't concatenate strings in loops | Optimizations on Java concatenation Strings are useless since JDK8 | [Github discussion with sources](https://github.com/green-code-initiative/creedengo-rules-specifications/issues/246) | diff --git a/src/main/rules/S6904/java/S6904.asciidoc b/src/main/rules/S6904/java/S6904.asciidoc new file mode 100644 index 000000000..79dd1e12c --- /dev/null +++ b/src/main/rules/S6904/java/S6904.asciidoc @@ -0,0 +1,106 @@ +:!sectids: + +== Why is this rule isn't a "GCIxxx" rule ? + +Because the rule already exists as built-in rule inside SonarQube with number S6904. +Some works were done by different people. +To not loose this work, we created this documentation to keep it. + +== Why is this an issue? + +When a JPA collection association is mapped with `FetchType.EAGER`, the persistence provider loads +the whole associated collection every time the owning entity is read, even when the application +never accesses it. + +On a collection, this is not a marginal cost: a single entity read can pull an arbitrary number of +additional rows from the database. This means more CPU cycles spent by the database engine, more +data transferred over the network, and more heap allocated by the application β€” all for objects +that may never be used. Less data loaded into memory means less energy consumed, both on the +database server and on the application server. + +`FetchType.LAZY` defers the load until the collection is actually accessed, which lets the +application pay the cost only when it needs the data. + +Note that `LAZY` is already the default for `@OneToMany` and `@ManyToMany`: this rule only reports +associations where `EAGER` has been set explicitly. + +== Examples + +=== Noncompliant + +[source,java,data-diff-id="1",data-diff-type="noncompliant"] +---- +@Entity +@Table(name = "purchase_order") +public class Order { + + @OneToMany(mappedBy = "order", fetch = FetchType.EAGER) // Noncompliant + private Set items = new HashSet<>(); + +} +---- + +[source,java,data-diff-id="2",data-diff-type="noncompliant"] +---- +@Entity +@Table(name = "purchase_order") +public class Order { + + @ManyToMany(fetch = FetchType.EAGER) // Noncompliant + private Set tags = new HashSet<>(); + +} +---- + +=== Compliant + +[source,java,data-diff-id="3",data-diff-type="compliant"] +---- +@Entity +@Table(name = "purchase_order") +public class Order { + + @OneToMany(mappedBy = "order", fetch = FetchType.LAZY) + private Set items = new HashSet<>(); + +} +---- + +[source,java,data-diff-id="4",data-diff-type="compliant"] +---- +@Entity +@Table(name = "purchase_order") +public class Order { + + @ManyToMany // Default fetch type for @ManyToMany is already LAZY + private Set tags = new HashSet<>(); + +} +---- + +== Exceptions to this rule + +This rule only applies to collection associations (`@OneToMany`, `@ManyToMany`). + +Single-valued associations (`@ManyToOne`, `@OneToOne`) default to `FetchType.EAGER` in JPA, but +their cost profile is different: they load one additional row, not an arbitrary number. Their real +impact appears when they are traversed in a loop, which is the N+1 selects problem β€” a separate +concern. + +No issue is raised when the `fetch` attribute is omitted on a collection association, since +`FetchType.LAZY` is already the JPA default in that case. + +== Resources + +=== Documentation + +- [Jakarta Persistence Specification](https://jakarta.ee/specifications/persistence/) - Default fetch types for association mappings +- [Hibernate User Guide](https://docs.jboss.org/hibernate/orm/current/userguide/html_single/Hibernate_User_Guide.html#fetching) - Fetching strategies + +=== Articles & blog posts + +- [Eager fetching is a code smell](https://vladmihalcea.com/eager-fetching-is-a-code-smell/) - Vlad Mihalcea + +=== Local measeures + +check complete analysis proving that this rule is relevant, here : diff --git a/src/main/rules/S6904/java/analysis/README.md b/src/main/rules/S6904/java/analysis/README.md new file mode 100644 index 000000000..4dc54fc84 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/README.md @@ -0,0 +1,474 @@ +# Measurement campaign β€” FetchType LAZY on JPA collections (S6904) + +Measurement harness for the rule "force FetchType.LAZY on JPA collections", superseding +the four historical PRs (#122, #125, #155, #325). The rule turned out to already exist as +a SonarQube built-in (`java:S6904`), so no new rule specification is added; this harness +produces the impact evidence kept as supporting documentation instead β€” see +[PR #487](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/487) +and `s6904-analysis-summary.md` at the root of this directory. + +**Status: both campaigns executed and analysed.** + +- **campaign1** β€” run on 2026-08-04, sizes 10 / 100 / 1000, 30 phases Γ— 30 iterations. + Archived under `level2-energytracer/campaign1/`; interpretation alongside it + (`campaign1-interpretation.md`). +- **campaign2** β€” run on 2026-08-05, sizes 10 / 30 / 50 / 100 / 500 / 1000, same protocol, + to locate the relevance threshold campaign1 left open. Archived under + `level2-energytracer/campaign2/`; interpretation alongside it + (`campaign2-interpretation.md`). Confirms campaign1 and discloses one genuine anomaly + around N=50–100 that campaign1's three points could not reveal β€” see that document, + Β§4–§5. + +--- + +## 1. Summary + +Two levels, numbered in their execution order β€” simplest and lightest first. + +| | Level 1 β€” JPA | Level 2 β€” energy | +|---|---|---| +| **Question** | How much useless work actually happens? | How much energy does that useless work cost? | +| **Measures** | SQL statements, hydrated rows, time, allocations | Joules (CPU / DRAM / GPU / ANE) | +| **Tool** | Hibernate Statistics + JDK | EnergyTracer, `mac` profiler | +| **Stack** | Real Hibernate 6 + H2 | Plain Java, simulated row source | +| **Duration** | a few minutes | ~45 min per size | +| **Machine** | any | Apple Silicon Mac, prepared and left undisturbed | +| **Role in review** | The functional proof | The energy proof | + +Both are needed, and they answer each other. Level 1 establishes *that* waste happens +and quantifies it in units any reviewer can check. Level 2 establishes *what that waste +costs* in energy. Neither is sufficient alone: level 1 measures no energy, and level 2 +runs neither SQL nor an ORM. + +### Why this split rather than a single measurement + +EnergyTracer's Java runner compiles with a bare `javac ` and runs with +`java -cp `. **No third-party library is reachable**: not Hibernate, not +H2, not even a JDBC driver. A JPA benchmark therefore cannot be measured directly by +EnergyTracer, and modifying the tool to make that possible is out of scope here β€” hence +two separate benches, each measuring what it can measure correctly. + +--- + +## 2. Layout + +``` +./ +β”œβ”€β”€ README.md this file: protocol and how-to +β”œβ”€β”€ s6904-analysis-summary.md PR #487 description, kept in sync with the analysis +β”œβ”€β”€ level1-jpa/ +β”‚ β”œβ”€β”€ pom.xml Hibernate 6.4 + H2 2.2, Java 17 +β”‚ β”œβ”€β”€ run.sh build + run +β”‚ β”œβ”€β”€ results/ generated: jpa-.txt +β”‚ └── src/main/java/org/greencodeinitiative/bench/ +β”‚ β”œβ”€β”€ FetchTypeBenchmark.java driver and instrumentation +β”‚ β”œβ”€β”€ eager/{Order,OrderItem}.java FetchType.EAGER mapping +β”‚ └── lazy/{Order,OrderItem}.java FetchType.LAZY mapping +β”‚ +└── level2-energytracer/ + β”œβ”€β”€ 1-generate.sh generates the variants + checks they compile + β”œβ”€β”€ 2-calibrate.sh (optional) tunes PARENT_READS for the machine + β”œβ”€β”€ 3-run_campaign.sh the campaign β€” writes to ./out-tmp and ./results + β”œβ”€β”€ 4-aggregate_report.sh consolidates the campaign into a single report + β”œβ”€β”€ 4-aggregate_report.py (the actual work: stats, plots, markdown) + β”‚ + β”œβ”€β”€ generate/ + β”‚ β”œβ”€β”€ template/Eager.java.tpl noncompliant variant (source of truth) + β”‚ β”œβ”€β”€ template/Lazy.java.tpl compliant variant (source of truth) + β”‚ └── variants/ generated: FetchType{Eager,Lazy}N.java + β”‚ (currently: N=10,30,50,100,500,1000) + β”‚ + β”œβ”€β”€ campaign1/ archived β€” sizes 10, 100, 1000 (2026-08-04) + β”‚ β”œβ”€β”€ campaign1-interpretation.md write-up: results, stats, caveats + β”‚ β”œβ”€β”€ out-tmp/ raw per-phase measurements, kept for traceability + β”‚ β”œβ”€β”€ results/n/ ET-analyzer's own per-size reports + β”‚ └── consolidated-report/ REPORT.md, data/, plots/, reports-source/ + β”‚ + └── campaign2/ archived β€” sizes 10,30,50,100,500,1000 (2026-08-05) + β”œβ”€β”€ campaign2-interpretation.md write-up: results, stats, caveats + β”œβ”€β”€ out-tmp/ raw per-phase measurements, kept for traceability + β”œβ”€β”€ results/n/ ET-analyzer's own per-size reports + └── consolidated-report/ REPORT.md, data/, plots/, reports-source/ +``` + +Between a run and its archiving, `3-run_campaign.sh` and `4-aggregate_report.sh` write +`out-tmp/`, `results/n/` and `consolidated-report/` directly under +`level2-energytracer/` β€” see Β§ 5, "Archiving a campaign", for the `mv` step that moves +them under `campaign/`. Nothing is currently sitting unarchived at that top level: both +campaigns above are the full history. + +Level 2's scripts are **prefixed by their launch order**. `2-calibrate.sh` is optional: +it only checks or tunes the bench's sizing on a new machine. `1-generate.sh` does not +need to be run by hand: `3-run_campaign.sh` triggers it automatically whenever a variant +is missing. + +The two JPA mappings are **identical except for one attribute** (`fetch = EAGER` vs +`LAZY`) and point at the same tables. Same at level 2: the two variants differ only in +the body of `loadOrder`. That is the condition for the comparison to mean anything. + +--- + +## 3. The scenario measured + +The one the rule targets, and only that one: + +> A parent entity owns a collection of children. The application loads the parent and +> **never accesses the collection**. + +- Under `EAGER`, the provider hydrates the whole collection anyway. +- Under `LAZY`, it only allocates the collection wrapper. + +Several sizes are swept, because the effect grows with collection size. A single figure +would say nothing; a sweep documents from which size the rule becomes relevant, which is +exactly what a demanding reviewer will ask for. + +| Campaign | Sizes | Purpose | +|---|---|---| +| campaign1 | 10, 100, 1000 | Establish that the effect exists, and its order of magnitude | +| campaign2 | 10, 30, 50, 100, 500, 1000 | Locate the relevance threshold between 10 and 100 | + +`1-generate.sh` maintains an **important invariant**: `PARENT_READS Γ— N β‰ˆ 5,000,000` +hydrated child rows per run, regardless of size. That is what makes the sizes comparable +at equal nominal work, and it is the basis of the "energy per hydrated row" figure, the +only quantity that is genuinely transposable. The `PARENT_READS` values for 10, 100 and +1000 are **identical to campaign1's**, so the two campaigns stay comparable on those +points. + +*Note: N=30 is the only inexact value (166,667 Γ— 30 = 5,000,010, 10 rows off out of five +million β€” negligible). No integer divides 5,000,000 exactly by 30.* + +### Conservative choices, to own in review + +Three decisions consistently **underestimate** the gap. Worth stating before anyone +objects: + +1. Level 2 allocates an empty `HashSet` on the LAZY side (modelling the Hibernate + wrapper) rather than `Collections.emptySet()`: the compliant variant is not free. +2. Both level-2 variants allocate the same child-row buffer, even though LAZY never reads + it: identical starting memory footprint. +3. The owning-side `@ManyToOne` is `LAZY` in both level-1 mappings, so no difference + sneaks in there. + +--- + +## 4. Preparing the machine + +This section applies **only to level 2**. Level 1 runs on any machine, no special care +needed. + +Taken from EnergyTracer's `EXPERIMENT_GUIDE.md`, itself based on LuΓ­s Cruz's guide. +**This step is not optional**: without it, background noise dwarfs the effect being +measured and the result is worthless. + +- [ ] Close **all** non-essential applications, including menu-bar utilities +- [ ] Disconnect unnecessary peripherals (external drives, printers…) +- [ ] Wired network, or no network at all +- [ ] Fix brightness, volume, power mode; disable sleep and screen saver +- [ ] Turn on "Do Not Disturb" +- [ ] **Plug in the power adapter** (a campaign runs for several hours) +- [ ] Stable ambient temperature, out of direct sunlight +- [ ] **Do not touch the machine** once the campaign has started + +Software prerequisites: + +- For level 1: Maven and a JDK 17+. +- For level 2: a **JDK** (not just a JRE β€” `javac` is required), + [`uv`](https://github.com/astral-sh/uv), and a clone of + `https://github.com/green-code-initiative/EnergyTracer` initialised with `./init.sh`. + +--- + +## 5. Running it + +The levels are numbered in execution order: increasing complexity and duration. + +### Level 1 β€” JPA (a few minutes) + +Fast, no requirement on the machine, and it tells you immediately whether the effect +exists. No point tying up a Mac for hours on an energy campaign if level 1 shows no gap. + +```bash +cd level1-jpa +./run.sh +``` + +Expected output: one table per size with, per parent read, the number of JDBC +statements, hydrated entities, loaded collections, time and allocated bytes β€” then the +EAGER/LAZY ratios, plus a reusable CSV block. + +### Level 2 β€” energy (budget ~45 min per size) + +Scripts are numbered in launch order. + +```bash +cd level2-energytracer + +./2-calibrate.sh # OPTIONAL: checks the bench's sizing +./3-run_campaign.sh ~/src/EnergyTracer mac # the campaign β€” calls 1-generate.sh if needed +./4-aggregate_report.sh ~/src/EnergyTracer # the consolidated report +``` + +`1-generate.sh` does not need to be run by hand: `3-run_campaign.sh` triggers it +automatically as soon as a variant is missing for one of the requested sizes. + +Sizes: default **10, 30, 50, 100, 500, 1000**. To restrict: + +```bash +./3-run_campaign.sh ~/src/EnergyTracer mac 10 100 1000 +``` + +**Duration.** About 45 minutes per size at default settings, half of it cooldown. Six +sizes therefore add up to **close to 5 hours** β€” best started at the end of the day. + +`2-calibrate.sh` is not decorative, even though it is optional. EnergyTracer launches +**one JVM per iteration**, and JVM startup costs 100 to 400 ms. If a run only lasts 20 ms, +95% of what is measured is JVM startup and the EAGER/LAZY gap disappears into the noise. +Aim for 300 to 500 ms per run. Rerun it on any new machine, or after changing +`PARENT_READS`. + +### Archiving a campaign + +`3-run_campaign.sh` always writes to `out-tmp/` and `results/`. Once the consolidated +report has been built, move the three directories into `campaign/` so the next +campaign starts clean: + +```bash +mkdir -p campaign2 && mv out-tmp results consolidated-report campaign2/ +``` + +An archived campaign stays analysable in place: + +```bash +./4-aggregate_report.sh ~/src/EnergyTracer -- \ + --out-tmp campaign1/out-tmp --output campaign1/consolidated-report +``` + +**Do not merge two campaigns** into one report: they run under different thermal and +system conditions. Comparing them size by size is legitimate and informative; +statistically pooling them is not. + +Campaign parameters, overridable via environment variable: + +| Variable | Default | Role | +|---|---|---| +| `WARMUP_PHASES` / `WARMUP_N` | 5 / 5 | Reach thermal steady state | +| `MEASURE_PHASES` / `MEASURE_N` | 30 / 30 | 900 samples per variant | +| `COOLDOWN` | 60 | Seconds between measurement phases | + +These values deliberately depart from EnergyTracer's own defaults (500 / 1000). Its +examples measure a few-millisecond arithmetic loop, entirely dominated by JVM startup, +hence the need for tens of thousands of samples. Here each run does ~400 ms of real work: +900 samples are ample, and the campaign fits in a few hours instead of several days. + +For a first exploratory pass: `MEASURE_PHASES=10 MEASURE_N=10 ./3-run_campaign.sh ...` +(~25 min). Do not publish those figures β€” they only serve to check the pipeline works. + +--- + +## 6. Interpreting and publishing + +`ET-analyzer` produces a Markdown report per size, under +`level2-energytracer/results/n/`, directly pastable into a PR: comparison table, +Welch's t-test (Ξ± = 0.05), Cohen's d, and a verdict. + +Raw measurements land in `level2-energytracer/out-tmp/`, outside the EnergyTracer clone, +which stays untouched. Once a campaign is archived under `campaign/`, this directory +is kept: it lets the report be regenerated, and it is the only record of the individual +measurements. + +Two pitfalls in the tool, both handled by `3-run_campaign.sh` β€” worth knowing if you +adapt the script: + +- **`-o` is not an output path**, it is a tag: `src/main.py` computes + `Path("output") / / `. A relative value therefore lands inside + the EnergyTracer clone. An **absolute** value overrides the whole prefix (`pathlib` + behaviour) and writes exactly where requested. +- **`ET-analyzer -p D` infers the profiler from the path component right after `D`** + (`profiler = parts[parts.index(first_folder) + 1]`). Pointing it one level too deep + does not error out: it produces a report labelled `raw` instead of `mac`. And pointing + it at the common root would merge every size into one meaningless report. Hence one + analysis per size, `-p out-tmp/fetchtype-n`. + +### Consolidating the campaign + +`ET-analyzer` produces one report per size, with no cross-size view and no plots. +`4-aggregate_report.sh` fills both gaps: + +```bash +cd level2-energytracer +./4-aggregate_report.sh ~/git_creedengo2/EnergyTracer +``` + +Passing the EnergyTracer clone avoids any installation: its virtual environment already +has pandas, numpy, scipy and matplotlib. Output under `consolidated-report/`: + +| | | +|---|---| +| `REPORT.md` | the consolidated document, ready to use for the PR | +| `data/` | every phase concatenated, with `size`, `phase`, `variant` columns added | +| `plots/n/` | box, violin, and **per-phase drift**, redrawn on the full sample | +| `plots/scaling_overhead.png` | the gap as a function of collection size | +| `reports-source/` | `ET-analyzer`'s own reports, unmodified, for traceability | + +Three additions over the tool's native output: + +- **Plots finally cover the whole campaign.** `ET`'s own plots are generated per phase, + so they only ever show one slice β€” pasting them into a PR would be misleading. +- **A per-phase drift plot.** If the curves climb across phases, the machine heated up or + was disturbed and the campaign should be rerun. Check this before anything else. +- **Mann-Whitney U and Cliff's delta**, alongside Welch's test. EnergyTracer's own guide + recommends them, because energy data is rarely normal. The report flags metrics where + Shapiro-Wilk rejects normality, and only concludes when both tests agree. + +The `--data-type` option defaults to `raw`, and that is the right choice: `raw` is +filtered once, globally, on the full aggregate, whereas `cleaned` has already been +filtered per phase on small samples β€” a weaker, doubly-filtered pipeline. + +The script also picks up data produced by the earlier version of `3-run_campaign.sh` +(without the `phase-` level), reported as a single phase. + +Then transcribe the figures into that campaign's `campaign-interpretation.md` β€” see +`campaign1-interpretation.md` and `campaign2-interpretation.md` for the structure to +follow (results, phase-level statistics, quality control, what not to claim, sources). + +### What to look at, in order + +1. **The direction of the gap.** A positive `Ξ” mean` means the smelly variant consumes + more. A negative or null gap on a given size is useful information, not a failure: it + documents the threshold below which the rule is not worth applying. +2. **The dominant component.** If the gap is carried by `dram_mj`, that is memory + allocation; by `cpu_mj`, that is hydration. This says *where* the energy goes. +3. **The variance.** Wide box plots signal a poorly controlled environment β€” redo the + machine preparation before interpreting anything. +4. **The trend with N.** This is the strongest argument: if the gap grows with collection + size, a causal mechanism is established, not just a correlation. + +### The trap to avoid + +With 900 samples per variant, a 0.4% gap comes out "statistically significant" with a +flattering Cohen's d. **Significant does not mean relevant.** Report the relative gap +next to the p-value, and do not defend the rule on a gain of a few tenths of a percent β€” +that would hand a careful reviewer an easy stick to beat it with. + +--- + +## 7. Limitations to state in the PR + +Hiding them would be both dishonest and counterproductive: a project reviewer will find +them anyway. Stating them upfront strengthens the measurement's credibility. + +**Level 1 β€” JPA** + +- In-memory H2: **no network latency, no disk I/O**. This is the least favourable setup + for the demonstration β€” on a remote RDBMS the gap would be markedly wider. Worth + saying, since it turns an apparent weakness into an argument. +- One session per read, so no first-level cache between reads. This is deliberate: + without it, the second read would be free and the measurement pointless. +- The second-level cache is disabled. +- Time and allocations are measured on a single thread, with no contention. + +**Level 2 β€” energy** + +- The row source is **simulated**: no database engine, no network, no JDBC driver. What + is measured is the client-side share (deserialisation, allocation, collection insert). + The database-server cost and the network transfer β€” likely the largest share of the + real saving β€” **are not measured**. The figure is therefore a **lower bound**. +- **The relative gap does not transpose to production.** Both variants do almost nothing + but hydration: the LAZY variant does almost nothing at all. An 80% gap means "hydrating + the collection is 80% of this synthetic program", not "a real application would consume + 80% less". In production this cost is diluted by SQL, network and business logic. + Publishing the **absolute energy per operation** is far more defensible than the + percentage. +- Each iteration launches a JVM. Absolute values include that startup; only the + **relative Java-vs-Java comparison** is valid, as EnergyTracer itself documents. +- Runs are short, so the JIT never reaches steady state: this is mostly interpreted or + C1-compiled code, not the hot C2 code of a long-running production service. +- The `mac` profiler reads Apple Silicon hardware counters. On another machine, only the + `carbon` profiler is available β€” an **estimate** (TDP Γ— CPU utilisation), never to be + presented as a hardware measurement. +- `ET-analyzer` applies a Welch t-test, which assumes near-normality. EnergyTracer's own + guide recommends complementing it with Mann-Whitney U, Cliff's delta and 95% confidence + intervals on the medians. For evidence meant to settle a core-team decision, do both. + +--- + +## 8. Harness validation status + +**Proven by campaign1** + +- Both levels run end to end: the Maven project builds, the variants compile, the + campaign and the aggregation produce their reports. +- The contract of EnergyTracer's Java runner, read from its source (`java_runner.py`, + `main.py`, `parser.py`, `analyzer.py`): classpath-free compilation, class-name detection + by regex, one JVM per iteration, checked exit code, no argument passed through, `-o` + read as a tag rather than a path, profiler inferred from the path component right after + the one passed to `-p`. +- The bench's sizing: `PARENT_READS` does place EAGER runs in the targeted window + (0.49 s at N=10 up to 0.67 s at N=1000). + +**Fixed along the way, worth knowing if you are rereading old reports** + +- An early version of `3-run_campaign.sh` reused the same output directory on every + phase. Since EnergyTracer writes its CSVs in overwrite mode, **only the last phase was + ever analysed**. Every phase now writes under its own `phase-/`. Any report predating + this fix covers a much smaller sample than it claims. + +**Unresolved, and worth a dedicated run** + +- **How much of the super-linearity is attributable to the simulated persistence + context.** `CONTEXT_SIZE` retains 1,024 parents, so the live set grows mechanically + with N, and no `-Xmx` is fixed. This is the most likely angle of attack in review on the + dossier's strongest argument. A sensitivity study on those two parameters would resolve + the ambiguity. +- **Repeating level 1**: its headline ratios (x12.65 in time, x32 in allocation) come from + a single run, with only 100 reads at N=1000, even though these are the figures put + forward. +- **The bimodality observed** over 2 to 8 phases depending on size (+23% to +48%), not + contiguous. It does not affect the relative gap, but its cause remains unknown. + +**Confirmed by campaign2, with one new open item** + +- Six sizes instead of three reproduce the relative gap within 0.6 points at the three + shared sizes (10, 100, 1000), across two independent measurement sessions run on + different days β€” see `campaign2-interpretation.md` Β§ 3. +- The intermediate sizes (30, 50, 500) do **not** fill in a smooth curve: energy per + hydrated row dips at N=100 instead of climbing monotonically, and the phase-level + correlation between EAGER and LAZY collapses at N=100 (+0.10, versus +0.73 to +0.97 + everywhere else). Three independent signals converge on this anomaly; none identifies a + mechanism. **Worth a dedicated re-run around N=50–150** β€” see `campaign2-interpretation.md` + Β§ 4–§ 5 before assuming it is settled. + +--- + +## 9. Where this landed in the specification + +This harness's output already feeds +[PR #487](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/487), +which supersedes the four historical PRs. Three things to know if you are picking this up +after a gap: + +1. **No new rule specification is added.** The rule already exists as the SonarQube + built-in `java:S6904`; adding a custom one under the same identifier is not viable. +2. **`src/main/rules/S6904/java/S6904.asciidoc` already exists** and documents why this + isn't a `GCIxxx` rule, with the usual "why is this an issue," compliant/noncompliant + examples and exceptions sections, plus a "Local measures" section pointing at this + harness's results. Keep it in sync if the analysis changes; do not assume it needs to be + created. +3. **`RULES.md`'s row for this rule already points to PR #487** (its legacy key, + `CRJVM205`, is kept β€” no `GCI116` row was ever added, despite that being the original + plan in the PR's first commit). + +`s6904-analysis-summary.md`, at the root of this directory, is the PR description kept in +sync with both campaigns β€” paste-ready, and the place to update if the analysis changes +again. + +Reminder: the project's `starter-pack.md` **does not require** an energy measurement. The +Definition of Done is purely software-based (unit tests, integration tests, CHANGELOG); +the only evidentiary requirement is "give references proving the benefit of the rule". An +EnergyTracer measurement answers that well beyond the project's norm β€” an asset to +highlight, not a box to tick. + +--- + +*Internal working document. Content intended for public publication on GitHub: review and +validate before releasing.* diff --git a/src/main/rules/S6904/java/analysis/level1-jpa/pom.xml b/src/main/rules/S6904/java/analysis/level1-jpa/pom.xml new file mode 100644 index 000000000..a3cbfb701 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level1-jpa/pom.xml @@ -0,0 +1,83 @@ + + + + + 4.0.0 + org.greencodeinitiative.bench + fetchtype-jpa-benchmark + 1.0.0 + jar + + + 17 + UTF-8 + 6.4.4.Final + 2.2.224 + + + + + org.hibernate.orm + hibernate-core + ${hibernate.version} + + + com.h2database + h2 + ${h2.version} + + + + org.slf4j + slf4j-nop + 2.0.13 + + + + + fetchtype-benchmark + + + org.apache.maven.plugins + maven-shade-plugin + 3.5.2 + + + package + shade + + + + org.greencodeinitiative.bench.FetchTypeBenchmark + + + + + + *:* + + META-INF/*.SF + META-INF/*.DSA + META-INF/*.RSA + + + + + + + + + + + diff --git a/src/main/rules/S6904/java/analysis/level1-jpa/results/level1-20260802-120143.txt b/src/main/rules/S6904/java/analysis/level1-jpa/results/level1-20260802-120143.txt new file mode 100644 index 000000000..6538cd77b --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level1-jpa/results/level1-20260802-120143.txt @@ -0,0 +1,26 @@ +GCI205 - JPA/Hibernate benchmark +java=21.0.11 vm=OpenJDK 64-Bit Server VM os=Mac OS X aarch64 +parent rows=200 + +fetch N reads stmt/read ent/read coll/read us/read KiB/read +---------------------------------------------------------------------------------------- +EAGER 10 2000 1,00 11,00 0,00 121,1 33,6 +LAZY 10 2000 1,00 1,00 0,00 67,0 24,4 +EAGER 100 200 1,00 101,00 0,00 253,9 105,1 +LAZY 100 200 1,00 1,00 0,00 68,1 24,1 +EAGER 1000 100 1,00 1001,00 0,00 968,5 764,1 +LAZY 1000 100 1,00 1,00 0,00 76,6 23,8 + +Overhead of EAGER over LAZY, per parent read: + N=10 time x1,81 allocation x1,38 rows hydrated +10 + N=100 time x3,73 allocation x4,37 rows hydrated +100 + N=1000 time x12,65 allocation x32,09 rows hydrated +1000 + +--- CSV --- +strategy,child_count,reads,statements,entity_loads,collection_fetches,elapsed_ns,allocated_bytes +EAGER,10,2000,2000,22000,0,242198333,68858584 +LAZY,10,2000,2000,2000,0,133940000,49931872 +EAGER,100,200,200,20200,0,50782833,21523744 +LAZY,100,200,200,200,0,13616042,4925568 +EAGER,1000,100,100,100100,0,96853000,78239448 +LAZY,1000,100,100,100,0,7658000,2438376 diff --git a/src/main/rules/S6904/java/analysis/level1-jpa/run.sh b/src/main/rules/S6904/java/analysis/level1-jpa/run.sh new file mode 100644 index 000000000..5e7b76d1f --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level1-jpa/run.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash +# Builds and runs the level 1 JPA/Hibernate benchmark. +# +# Usage: +# ./run.sh # sizes 10,100,1000 with 200 parent rows +# ./run.sh 10,100,1000 200 +# +# Output goes to stdout and to results/jpa-.txt (table + CSV). +set -euo pipefail +cd "$(dirname "$0")" + +command -v mvn >/dev/null || { echo "maven not found"; exit 1; } + +mvn -q clean package + +mkdir -p results +stamp="$(date +%Y%m%d-%H%M%S)" +out="results/jpa-${stamp}.txt" + +java -jar target/fetchtype-benchmark.jar "$@" | tee "$out" + +echo +echo "Saved to $out" diff --git a/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/FetchTypeBenchmark.java b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/FetchTypeBenchmark.java new file mode 100644 index 000000000..c61d21de4 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/FetchTypeBenchmark.java @@ -0,0 +1,260 @@ +package org.greencodeinitiative.bench; + +import com.sun.management.ThreadMXBean; +import java.lang.management.ManagementFactory; +import java.util.ArrayList; +import java.util.List; +import java.util.Locale; +import java.util.function.ToLongFunction; +import org.hibernate.Session; +import org.hibernate.SessionFactory; +import org.hibernate.cfg.Configuration; +import org.hibernate.stat.Statistics; + +/** + * FetchType LAZY - level 1 benchmark: measures the cost of {@code FetchType.EAGER} versus + * {@code FetchType.LAZY} on a JPA collection association, on a real Hibernate + H2 stack, + * for the case the rule targets: the application loads the parent and never touches + * the collection. + * + *

What is measured, per parent read: + *

    + *
  • JDBC statements prepared
    • + *
  • entities hydrated and collections fetched (Hibernate statistics)
    • + *
  • wall time
    • + *
  • bytes allocated on the heap
    • + *
+ * + *

These are functional proxies, not energy. Energy is measured separately at level 2 + * with EnergyTracer. Both are needed: this one shows how much useless work is done, + * the other shows what that work costs in joules. + * + *

A fresh session is opened for every read, so the first-level cache never turns the + * second read into a no-op. That mirrors one session per request in a real application. + * + *

Usage: {@code java -jar fetchtype-benchmark.jar [size,size,...] [parentRows]} + */ +public final class FetchTypeBenchmark { + + private static final int DEFAULT_PARENT_ROWS = 200; + private static final double WARMUP_RATIO = 0.25; + + private FetchTypeBenchmark() { + } + + public static void main(String[] args) { + int[] sizes = args.length > 0 ? parseSizes(args[0]) : new int[] {10, 100, 1000}; + int parentRows = args.length > 1 ? Integer.parseInt(args[1]) : DEFAULT_PARENT_ROWS; + + System.out.println("FetchType LAZY on JPA collections - benchmark"); + System.out.println("java=" + System.getProperty("java.version") + + " vm=" + System.getProperty("java.vm.name") + + " os=" + System.getProperty("os.name") + " " + System.getProperty("os.arch")); + System.out.println("parent rows=" + parentRows); + System.out.println(); + + List results = new ArrayList<>(); + for (int size : sizes) { + int reads = Math.max(100, 20_000 / size); + results.add(run("EAGER", size, parentRows, reads, + org.greencodeinitiative.bench.eager.Order.class, + o -> o.getId() + o.getReference().length())); + results.add(run("LAZY", size, parentRows, reads, + org.greencodeinitiative.bench.lazy.Order.class, + o -> o.getId() + o.getReference().length())); + } + + printTable(results); + printCsv(results); + } + + private static Result run(String strategy, + int childCount, + int parentRows, + int reads, + Class orderType, + ToLongFunction probe) { + + String dbName = "fetchtype_" + strategy.toLowerCase() + "_" + childCount; + Class itemType = "EAGER".equals(strategy) + ? org.greencodeinitiative.bench.eager.OrderItem.class + : org.greencodeinitiative.bench.lazy.OrderItem.class; + + try (SessionFactory sf = sessionFactory(dbName, orderType, itemType)) { + seed(sf, strategy, childCount, parentRows); + + int warmup = Math.max(10, (int) (reads * WARMUP_RATIO)); + readLoop(sf, orderType, probe, warmup, parentRows); + + System.gc(); + sleepQuietly(200); + + Statistics stats = sf.getStatistics(); + stats.clear(); + ThreadMXBean threads = (ThreadMXBean) ManagementFactory.getThreadMXBean(); + long allocBefore = threads.getCurrentThreadAllocatedBytes(); + long start = System.nanoTime(); + + readLoop(sf, orderType, probe, reads, parentRows); + + long elapsedNanos = System.nanoTime() - start; + long allocated = threads.getCurrentThreadAllocatedBytes() - allocBefore; + + return new Result( + strategy, + childCount, + reads, + stats.getPrepareStatementCount(), + stats.getEntityLoadCount(), + stats.getCollectionFetchCount(), + elapsedNanos, + allocated); + } + } + + private static void readLoop(SessionFactory sf, + Class orderType, + ToLongFunction probe, + int iterations, + int parentRows) { + long sink = 0; + for (int i = 0; i < iterations; i++) { + long id = (i % parentRows) + 1L; + try (Session session = sf.openSession()) { + T order = session.get(orderType, id); + // Only the parent is used. The collection is deliberately never accessed. + sink += probe.applyAsLong(order); + } + } + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static SessionFactory sessionFactory(String dbName, Class... entities) { + Configuration cfg = new Configuration(); + cfg.setProperty("hibernate.connection.driver_class", "org.h2.Driver"); + cfg.setProperty("hibernate.connection.url", + "jdbc:h2:mem:" + dbName + ";DB_CLOSE_DELAY=-1"); + cfg.setProperty("hibernate.connection.username", "sa"); + cfg.setProperty("hibernate.connection.password", ""); + cfg.setProperty("hibernate.dialect", "org.hibernate.dialect.H2Dialect"); + cfg.setProperty("hibernate.hbm2ddl.auto", "create-drop"); + cfg.setProperty("hibernate.generate_statistics", "true"); + cfg.setProperty("hibernate.show_sql", "false"); + cfg.setProperty("hibernate.format_sql", "false"); + cfg.setProperty("hibernate.cache.use_second_level_cache", "false"); + cfg.setProperty("hibernate.cache.use_query_cache", "false"); + // Seeding only: keeps the fixture build from dominating the run. Not measured. + cfg.setProperty("hibernate.jdbc.batch_size", "100"); + cfg.setProperty("hibernate.order_inserts", "true"); + for (Class entity : entities) { + cfg.addAnnotatedClass(entity); + } + return cfg.buildSessionFactory(); + } + + private static void seed(SessionFactory sf, String strategy, int childCount, int parentRows) { + try (Session session = sf.openSession()) { + session.beginTransaction(); + long itemId = 1L; + for (int p = 1; p <= parentRows; p++) { + if ("EAGER".equals(strategy)) { + var order = new org.greencodeinitiative.bench.eager.Order((long) p, "ORD-" + p); + for (int c = 0; c < childCount; c++) { + order.getItems().add(new org.greencodeinitiative.bench.eager.OrderItem( + itemId, "ITEM-" + itemId, 1 + (int) (itemId % 7), order)); + itemId++; + } + session.persist(order); + } else { + var order = new org.greencodeinitiative.bench.lazy.Order((long) p, "ORD-" + p); + for (int c = 0; c < childCount; c++) { + order.getItems().add(new org.greencodeinitiative.bench.lazy.OrderItem( + itemId, "ITEM-" + itemId, 1 + (int) (itemId % 7), order)); + itemId++; + } + session.persist(order); + } + if (p % 20 == 0) { + session.flush(); + session.clear(); + } + } + session.getTransaction().commit(); + } + } + + private static void printTable(List results) { + // Locale.ROOT throughout: printf's %f is locale-sensitive by default (a JVM + // running under a French locale prints "1,81" instead of "1.81"), which silently + // breaks anything downstream that parses this output as plain numbers. + System.out.printf(Locale.ROOT, "%-7s %6s %7s %11s %11s %11s %12s %12s%n", + "fetch", "N", "reads", "stmt/read", "ent/read", "coll/read", "us/read", "KiB/read"); + System.out.println("-".repeat(88)); + for (Result r : results) { + System.out.printf(Locale.ROOT, "%-7s %6d %7d %11.2f %11.2f %11.2f %12.1f %12.1f%n", + r.strategy, r.childCount, r.reads, + r.statements / (double) r.reads, + r.entityLoads / (double) r.reads, + r.collectionFetches / (double) r.reads, + r.elapsedNanos / 1_000.0 / r.reads, + r.allocatedBytes / 1024.0 / r.reads); + } + System.out.println(); + + System.out.println("Overhead of EAGER over LAZY, per parent read:"); + for (int i = 0; i + 1 < results.size(); i += 2) { + Result eager = results.get(i); + Result lazy = results.get(i + 1); + System.out.printf(Locale.ROOT, " N=%-5d time x%.2f allocation x%.2f rows hydrated +%d%n", + eager.childCount, + ratio(eager.elapsedNanos, lazy.elapsedNanos), + ratio(eager.allocatedBytes, lazy.allocatedBytes), + (eager.entityLoads - lazy.entityLoads) / eager.reads); + } + System.out.println(); + } + + private static void printCsv(List results) { + System.out.println("--- CSV ---"); + System.out.println("strategy,child_count,reads,statements,entity_loads," + + "collection_fetches,elapsed_ns,allocated_bytes"); + for (Result r : results) { + System.out.printf("%s,%d,%d,%d,%d,%d,%d,%d%n", + r.strategy, r.childCount, r.reads, r.statements, r.entityLoads, + r.collectionFetches, r.elapsedNanos, r.allocatedBytes); + } + } + + private static double ratio(long a, long b) { + return b == 0 ? Double.NaN : a / (double) b; + } + + private static int[] parseSizes(String arg) { + String[] parts = arg.split(","); + int[] sizes = new int[parts.length]; + for (int i = 0; i < parts.length; i++) { + sizes[i] = Integer.parseInt(parts[i].trim()); + } + return sizes; + } + + private static void sleepQuietly(long millis) { + try { + Thread.sleep(millis); + } catch (InterruptedException e) { + Thread.currentThread().interrupt(); + } + } + + private record Result(String strategy, + int childCount, + int reads, + long statements, + long entityLoads, + long collectionFetches, + long elapsedNanos, + long allocatedBytes) { + } +} diff --git a/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/eager/Order.java b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/eager/Order.java new file mode 100644 index 000000000..7a13399ca --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/eager/Order.java @@ -0,0 +1,52 @@ +package org.greencodeinitiative.bench.eager; + +import jakarta.persistence.CascadeType; +import jakarta.persistence.Entity; +import jakarta.persistence.FetchType; +import jakarta.persistence.Id; +import jakarta.persistence.OneToMany; +import jakarta.persistence.Table; +import java.util.HashSet; +import java.util.Set; + +/** + * NONCOMPLIANT mapping: the collection is fetched eagerly. + * + *

Strictly identical to {@code org.greencodeinitiative.bench.lazy.Order} except for the + * {@code fetch} attribute below. Both are mapped onto the same tables and are loaded through + * two separate SessionFactories. + */ +@Entity +@Table(name = "ORDERS") +public class Order { + + @Id + private Long id; + + private String reference; + + // The one line under test. + @OneToMany(mappedBy = "order", fetch = FetchType.EAGER, cascade = CascadeType.ALL) + private Set items = new HashSet<>(); + + protected Order() { + // required by JPA + } + + public Order(Long id, String reference) { + this.id = id; + this.reference = reference; + } + + public Long getId() { + return id; + } + + public String getReference() { + return reference; + } + + public Set getItems() { + return items; + } +} diff --git a/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/eager/OrderItem.java b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/eager/OrderItem.java new file mode 100644 index 000000000..bc946b450 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/eager/OrderItem.java @@ -0,0 +1,41 @@ +package org.greencodeinitiative.bench.eager; + +import jakarta.persistence.Entity; +import jakarta.persistence.FetchType; +import jakarta.persistence.Id; +import jakarta.persistence.JoinColumn; +import jakarta.persistence.ManyToOne; +import jakarta.persistence.Table; + +@Entity +@Table(name = "ORDER_ITEMS") +public class OrderItem { + + @Id + private Long id; + + private String label; + + private int quantity; + + // Kept LAZY on both sides: the rule under test targets collection associations only, + // so the owning side must not introduce a difference between the two variants. + @ManyToOne(fetch = FetchType.LAZY) + @JoinColumn(name = "ORDER_ID") + private Order order; + + protected OrderItem() { + // required by JPA + } + + public OrderItem(Long id, String label, int quantity, Order order) { + this.id = id; + this.label = label; + this.quantity = quantity; + this.order = order; + } + + public Long getId() { + return id; + } +} diff --git a/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/lazy/Order.java b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/lazy/Order.java new file mode 100644 index 000000000..59bb9e651 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/lazy/Order.java @@ -0,0 +1,51 @@ +package org.greencodeinitiative.bench.lazy; + +import jakarta.persistence.CascadeType; +import jakarta.persistence.Entity; +import jakarta.persistence.FetchType; +import jakarta.persistence.Id; +import jakarta.persistence.OneToMany; +import jakarta.persistence.Table; +import java.util.HashSet; +import java.util.Set; + +/** + * COMPLIANT mapping: the collection is fetched lazily. + * + *

Strictly identical to {@code org.greencodeinitiative.bench.eager.Order} except for the + * {@code fetch} attribute below. + */ +@Entity +@Table(name = "ORDERS") +public class Order { + + @Id + private Long id; + + private String reference; + + // The one line under test. + @OneToMany(mappedBy = "order", fetch = FetchType.LAZY, cascade = CascadeType.ALL) + private Set items = new HashSet<>(); + + protected Order() { + // required by JPA + } + + public Order(Long id, String reference) { + this.id = id; + this.reference = reference; + } + + public Long getId() { + return id; + } + + public String getReference() { + return reference; + } + + public Set getItems() { + return items; + } +} diff --git a/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/lazy/OrderItem.java b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/lazy/OrderItem.java new file mode 100644 index 000000000..2751fddbd --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level1-jpa/src/main/java/org/greencodeinitiative/bench/lazy/OrderItem.java @@ -0,0 +1,41 @@ +package org.greencodeinitiative.bench.lazy; + +import jakarta.persistence.Entity; +import jakarta.persistence.FetchType; +import jakarta.persistence.Id; +import jakarta.persistence.JoinColumn; +import jakarta.persistence.ManyToOne; +import jakarta.persistence.Table; + +@Entity +@Table(name = "ORDER_ITEMS") +public class OrderItem { + + @Id + private Long id; + + private String label; + + private int quantity; + + // Kept LAZY on both sides: the rule under test targets collection associations only, + // so the owning side must not introduce a difference between the two variants. + @ManyToOne(fetch = FetchType.LAZY) + @JoinColumn(name = "ORDER_ID") + private Order order; + + protected OrderItem() { + // required by JPA + } + + public OrderItem(Long id, String label, int quantity, Order order) { + this.id = id; + this.label = label; + this.quantity = quantity; + this.order = order; + } + + public Long getId() { + return id; + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/1-generate.sh b/src/main/rules/S6904/java/analysis/level2-energytracer/1-generate.sh new file mode 100755 index 000000000..f8e958ffd --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/1-generate.sh @@ -0,0 +1,61 @@ +#!/usr/bin/env bash +# Generates the Java variants (one pair per collection size: EAGER + LAZY) from the two +# templates. Regenerate after editing a template so the compliant and noncompliant +# variants never drift apart. +# +# Usage: ./1-generate.sh +# +# Output: generate/variants/FetchType{Eager,Lazy}N.java +set -euo pipefail + +cd "$(dirname "$0")" +mkdir -p generate/variants + +# size:parent_reads +# +# parent_reads is calibrated on two constraints: +# +# 1. One JVM run of the EAGER variant should last ~300-500 ms, so that JVM startup +# (~100-400 ms, paid once per iteration) does not drown the signal. +# Re-run ./2-calibrate.sh on the target machine and adjust if needed. +# +# 2. parent_reads x size is kept at ~5,000,000 hydrated child rows for every size. +# This is what makes the sizes comparable at equal nominal work, and it is the +# basis of the "energy per hydrated row" figure. Do not break it casually. +# N=30 is the only inexact one (166,667 x 30 = 5,000,010, off by 10 rows). +# +# The values for 10, 100 and 1000 are identical to those of campaign1: keeping them +# unchanged is what allows a later campaign to be compared with the first one. +CONFIGS=( + "10:500000" + "30:166667" + "50:100000" + "100:50000" + "500:10000" + "1000:5000" +) + +for cfg in "${CONFIGS[@]}"; do + n="${cfg%%:*}" + reads="${cfg##*:}" + + for variant in Eager Lazy; do + class="FetchType${variant}N${n}" + out="generate/variants/${class}.java" + sed -e "s/__CLASS__/${class}/g" \ + -e "s/__CHILD_COUNT__/${n}/g" \ + -e "s/__PARENT_READS__/${reads}/g" \ + "generate/template/${variant}.java.tpl" > "$out" + printf 'generated %-46s (CHILD_COUNT=%-4s PARENT_READS=%-7s rows=%s)\n' \ + "$out" "$n" "$reads" "$((n * reads))" + done +done + +echo +echo "Sanity check: compiling every variant." +tmp="$(mktemp -d)" +trap 'rm -rf "$tmp"' EXIT +for f in generate/variants/*.java; do + javac -d "$tmp" "$f" +done +echo "All variants compile." diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/2-calibrate.sh b/src/main/rules/S6904/java/analysis/level2-energytracer/2-calibrate.sh new file mode 100755 index 000000000..bb741c18c --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/2-calibrate.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash +# Times one JVM run of each variant, so PARENT_READS can be tuned for the target machine. +# +# Why this matters: EnergyTracer launches one JVM per iteration, and JVM startup costs +# ~100-400 ms. If a run only lasts 20 ms, 95% of what you measure is JVM startup and the +# EAGER/LAZY difference disappears into the noise. Aim for a run of roughly 300-500 ms. +# +# Usage: ./2-calibrate.sh +set -euo pipefail +cd "$(dirname "$0")" + +command -v javac >/dev/null || { echo "javac not found: install a JDK (not just a JRE)"; exit 1; } + +[ -d generate/variants ] || ./1-generate.sh + +build="$(mktemp -d)" +trap 'rm -rf "$build"' EXIT +javac -d "$build" generate/variants/*.java + +echo +printf '%-26s %10s %10s\n' "variant" "run 1" "run 2" +printf '%-26s %10s %10s\n' "--------------------------" "----------" "----------" + +for f in generate/variants/*.java; do + cls="$(basename "$f" .java)" + times=() + for _ in 1 2; do + start=$(python3 -c 'import time;print(int(time.time()*1000))') + java -cp "$build" "$cls" + end=$(python3 -c 'import time;print(int(time.time()*1000))') + times+=("$((end - start)) ms") + done + printf '%-26s %10s %10s\n' "$cls" "${times[0]}" "${times[1]}" +done + +cat <<'EOF' + +Reading the table +----------------- +These timings include JVM startup (~100-400 ms), so subtract roughly that much to get +the actual workload duration. + + workload well under 300 ms -> raise PARENT_READS in 1-generate.sh, then re-run ./1-generate.sh + workload well over 800 ms -> lower it, otherwise the campaign takes too long + +The EAGER and LAZY variants of the same size MUST keep the same PARENT_READS value. +EOF diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/3-run_campaign.sh b/src/main/rules/S6904/java/analysis/level2-energytracer/3-run_campaign.sh new file mode 100755 index 000000000..c8fed3ecc --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/3-run_campaign.sh @@ -0,0 +1,195 @@ +#!/usr/bin/env bash +# Runs the full EnergyTracer campaign for FetchType LAZY, one campaign per collection size. +# +# Reimplements the protocol of EnergyTracer's own run_experiment.sh (warm-up phases, +# measurement phases, cooldown, shuffled order) because that script does not expose +# -f1/-f2 and therefore cannot point at our variants. +# +# Usage: +# ./3-run_campaign.sh [profiler] [sizes...] +# +# profiler : mac (Apple Silicon hardware counters) | carbon (CodeCarbon estimate) +# default: mac +# sizes : any of 10 30 50 100 500 1000 default: 10 30 50 100 500 1000 +# +# Example: +# ./3-run_campaign.sh ~/git_creedengo2/EnergyTracer mac 10 30 50 100 500 1000 +# +# Before running, go through the checklist in ../README.md section "Preparing the machine". +# +# Campaign archiving convention +# ----------------------------- +# This script always writes to ./out-tmp and ./results. Once a campaign is finished and +# its report has been built, move both directories plus consolidated-report into +# ./campaign/ so the next campaign starts clean. Past campaigns stay analysable in place: +# +# ./4-aggregate_report.sh -- \ +# --out-tmp campaign1/out-tmp --output campaign1/consolidated-report +# +# +# Where the output goes, and why it is built this way +# --------------------------------------------------- +# Two constraints from EnergyTracer's source dictate the layout below. +# +# 1. `ET -o X` is NOT an output path. src/main.py does: +# output_directory = Path("output") / args.profiler / args.output_dir +# so a bare `-o mytag` lands in /output//mytag/{raw,cleaned}. +# Because pathlib drops the left-hand parts when the right-hand one is absolute, +# passing an ABSOLUTE value to -o overrides the whole prefix and writes exactly +# there. That is what we do, so nothing is ever written inside the ET checkout. +# +# 2. `ET-analyzer -p D` infers the profiler from the path component that immediately +# follows D (src/analyzer.py: profiler = parts[parts.index(first_folder) + 1]). +# So D must be the parent of a directory named after the profiler. Pointing the +# analyzer one level too deep does not crash - it silently labels the report +# "raw" instead of "mac", which is worse. Nothing below that point matters to the +# analyzer, which walks the whole subtree with rglob("*.csv") - hence point 3. +# +# 3. `save_history` (src/utilities/save_csv.py) opens its CSV with mode "w": every +# call OVERWRITES the previous one at the same path. Upstream's own +# run_experiment.sh gives every phase a distinct tag (`-o "measure-$i"`) for +# exactly this reason. A first version of this script reused the same -o value +# across all measurement phases, so every phase silently erased the previous +# one and only the LAST phase's samples ever reached the analyzer. Fixed below: +# each phase gets its own "phase-$i" subdirectory. The analyzer does not care +# how deep the tree goes past , so this does not break point 2. +# +# Hence the layout, all absolute: +# +# out-tmp/fetchtype-n//phase-/{raw,cleaned}/*.csv <- one shard per phase, written by ET +# out-tmp/fetchtype-n/results///*.md <- written by ET-analyzer (merges all phases) +# ^ analyzer is pointed here, one level above +# +# Analysing per size rather than globally is deliberate: pointing the analyzer at the +# common root would merge all three collection sizes into a single meaningless report. +set -euo pipefail + +HERE="$(cd "$(dirname "$0")" && pwd)" + +ET_DIR="${1:-}" +if [ -z "$ET_DIR" ] || [ ! -d "$ET_DIR" ]; then + echo "usage: $0 [mac|carbon] [sizes...]" >&2 + exit 1 +fi +ET_DIR="$(cd "$ET_DIR" && pwd)" +shift + +PROFILER="${1:-mac}" +case "$PROFILER" in + mac|carbon) shift || true ;; + *) PROFILER="mac" ;; +esac + +SIZES=("$@") +[ ${#SIZES[@]} -eq 0 ] && SIZES=(10 30 50 100 500 1000) + +# Phase parameters. Deliberately different from EnergyTracer's defaults (500/1000): +# our workload is ~400 ms per JVM run instead of a few ms, so far fewer iterations +# per phase are needed to reach the same number of samples, and the campaign stays +# within a few hours instead of tens of hours. +WARMUP_PHASES="${WARMUP_PHASES:-5}" +WARMUP_N="${WARMUP_N:-5}" +MEASURE_PHASES="${MEASURE_PHASES:-30}" +MEASURE_N="${MEASURE_N:-30}" +COOLDOWN="${COOLDOWN:-60}" + +# Scratch area for raw measurements. Absolute, and outside the ET checkout. +OUT_ROOT="${OUT_ROOT:-$HERE/out-tmp}" + +command -v uv >/dev/null || { echo "uv not found in PATH" >&2; exit 1; } + +# Regenerate if any requested size is missing, not merely if the directory is absent: +# after adding a size to 1-generate.sh, the directory still exists but is incomplete, +# and the campaign would fail several minutes in on the first missing variant. +need_generate=0 +for n in "${SIZES[@]}"; do + [ -f "$HERE/generate/variants/FetchTypeEagerN${n}.java" ] && + [ -f "$HERE/generate/variants/FetchTypeLazyN${n}.java" ] || need_generate=1 +done +if [ "$need_generate" = 1 ]; then + echo "Some variants are missing for the requested sizes - regenerating." + "$HERE/1-generate.sh" + echo +fi + +mkdir -p "$OUT_ROOT" +OUT_ROOT="$(cd "$OUT_ROOT" && pwd)" + +echo "EnergyTracer : $ET_DIR" +echo "Profiler : $PROFILER" +echo "Sizes : ${SIZES[*]}" +echo "Scratch dir : $OUT_ROOT" +echo "Warm-up : $WARMUP_PHASES phases x $WARMUP_N iterations" +echo "Measurement : $MEASURE_PHASES phases x $MEASURE_N iterations (= $((MEASURE_PHASES * MEASURE_N)) samples per variant)" +echo "Cooldown : ${COOLDOWN}s between measurement phases" +echo +echo "Do not touch this machine until the campaign is over." +echo + +cd "$ET_DIR" + +for n in "${SIZES[@]}"; do + f1="$HERE/generate/variants/FetchTypeEagerN${n}.java" + f2="$HERE/generate/variants/FetchTypeLazyN${n}.java" + [ -f "$f1" ] && [ -f "$f2" ] || { + echo "no variant for size $n - add it to CONFIGS in 1-generate.sh" >&2; exit 1; } + + size_dir="$OUT_ROOT/fetchtype-n${n}" # analyzer input: parent of + data_dir="$size_dir/$PROFILER" # ET output: gets /raw and /cleaned appended + warm_dir="$OUT_ROOT/warmup-n${n}/$PROFILER" + + # Start each size from a clean slate, otherwise CSVs from a previous campaign + # would be silently merged into this one's report. + rm -rf "$size_dir" "$OUT_ROOT/warmup-n${n}" + mkdir -p "$data_dir" "$warm_dir" + [ -d "$data_dir" ] || { echo "could not create $data_dir" >&2; exit 1; } + + echo "=== size N=${n} : warm-up ===" + for i in $(seq 1 "$WARMUP_PHASES"); do + echo " warm-up $i/$WARMUP_PHASES" + uv run ET -p "$PROFILER" -n "$WARMUP_N" -f1 "$f1" -f2 "$f2" -o "$warm_dir" --shuffle + done + rm -rf "$OUT_ROOT/warmup-n${n}" # warm-up data is discarded, as upstream does + + echo "=== size N=${n} : measurement ===" + for i in $(seq 1 "$MEASURE_PHASES"); do + echo " phase $i/$MEASURE_PHASES" + # Each phase writes to its own subdirectory (see point 3 above): reusing + # $data_dir directly here would make every phase overwrite the last one. + uv run ET -p "$PROFILER" -n "$MEASURE_N" -f1 "$f1" -f2 "$f2" -o "$data_dir/phase-$i" --shuffle + if [ "$i" -lt "$MEASURE_PHASES" ]; then + sleep "$COOLDOWN" + fi + done + + # Fail loudly here rather than let the analyzer report an opaque "does not exist". + csv_count="$(find "$data_dir" -name '*.csv' -type f 2>/dev/null | wc -l | tr -d ' ')" + if [ "$csv_count" -eq 0 ]; then + echo "ERROR: no CSV produced under $data_dir" >&2 + echo "Contents of $size_dir:" >&2 + find "$size_dir" -maxdepth 3 2>/dev/null | sed 's|^| |' >&2 + echo "Check the ET output above: a failed javac or a non-zero exit code aborts a phase." >&2 + exit 1 + fi + echo " $csv_count CSV files collected" + + echo "=== size N=${n} : analysis ===" + uv run ET-analyzer -p "$size_dir" -v + + res="$HERE/results/n${n}" + rm -rf "$res" + mkdir -p "$res" + if [ -d "$size_dir/results" ]; then + cp -R "$size_dir/results/." "$res"/ + echo " reports copied to $res" + else + echo " WARNING: analyzer produced no results/ directory under $size_dir" >&2 + fi + echo +done + +echo "Campaign finished." +echo " reports : $HERE/results//" +echo " raw data : $OUT_ROOT/ (scratch - safe to delete once the reports are saved)" +echo +echo "Fill in ../results-template.md with the numbers, then attach it to the PR." diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/4-aggregate_report.py b/src/main/rules/S6904/java/analysis/level2-energytracer/4-aggregate_report.py new file mode 100644 index 000000000..1ddc8f055 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/4-aggregate_report.py @@ -0,0 +1,712 @@ +#!/usr/bin/env python3 +""" +Builds a single consolidated report directory from a full EnergyTracer campaign. + +Why this exists +--------------- +EnergyTracer splits its output in a way that is fine for one run but awkward for a +campaign made of many phases: + +* `ET` writes CSVs and plots once per phase, under + `//phase-/{raw,cleaned}/{csv,plots}/`. The plots therefore only + ever show ONE phase - a small slice of the campaign - which makes them misleading + to paste into a pull request. +* `ET-analyzer` merges the CSVs and writes a Markdown report, but never re-plots, and + produces one report per (size, data type) with no cross-size view. + +This script closes both gaps: it concatenates every phase, recomputes the statistics on +the whole sample, redraws the plots from the complete data, and assembles everything - +including EnergyTracer's own reports, kept verbatim - into one directory. + +Statistics +---------- +The mean/Welch/Cohen's d pipeline mirrors `src/analysis/statistical_analysis.py` of +EnergyTracer so the numbers stay comparable with its reports (same 3-sigma z-score +outlier removal, same pooled-std Cohen's d, same alpha, same delta convention). + +It adds Mann-Whitney U and Cliff's delta, which EnergyTracer's own EXPERIMENT_GUIDE +recommends for energy data - energy samples are rarely normally distributed, and a +Welch t-test alone is easy to attack in review. + +Usage +----- + ./4-aggregate_report.sh ~/git_creedengo2/EnergyTracer # recommended wrapper + python3 4-aggregate_report.py --help # direct, needs the deps + +Requires pandas, numpy, scipy and matplotlib - all already present in the EnergyTracer +virtualenv, which is what the wrapper script uses. +""" + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path +import platform +import re +import shutil +import sys + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +from scipy import stats # noqa: E402 + +# ── Constants kept aligned with EnergyTracer ────────────────────────────────── + +METRICS = ("cpu_mj", "gpu_mj", "ane_mj", "dram_mj", "time_s") +ENERGY_METRICS = ("cpu_mj", "gpu_mj", "dram_mj", "ane_mj") +ALPHA = 0.05 +ZSCORE_THRESHOLD = 3 +MIN_SAMPLE_SIZE = 25 + +FIGURE_SIZE = (10, 6) +FIGURE_DPI = 150 + +LABEL_WITH = "EAGER (with smell)" +LABEL_WITHOUT = "LAZY (without smell)" + + +# ── Statistics (mirrors src/analysis/statistical_analysis.py) ───────────────── + + +def remove_outliers_zscore(values: list[float]) -> list[float]: + """Drop points more than 3 standard deviations from the mean.""" + if len(values) < 2: + return list(values) + arr = np.asarray(values, dtype=float) + std = np.std(arr, ddof=1) + if std == 0: + return list(values) + mean = np.mean(arr) + return [x for x in values if abs(mean - x) <= ZSCORE_THRESHOLD * std] + + +def cohens_d(a: list[float], b: list[float]) -> float: + """Effect size using the pooled standard deviation, as EnergyTracer defines it.""" + x, y = np.asarray(a, dtype=float), np.asarray(b, dtype=float) + if np.ptp(x) == 0 and np.ptp(y) == 0: + return 0.0 + pooled = np.sqrt((np.std(x, ddof=1) ** 2 + np.std(y, ddof=1) ** 2) / 2) + return 0.0 if pooled == 0 else float((np.mean(x) - np.mean(y)) / pooled) + + +def effect_label(d: float) -> str: + a = abs(d) + if a < 0.2: + return "negligible" + if a < 0.5: + return "small" + if a < 0.8: + return "medium" + return "large" + + +def cliffs_delta(a: list[float], b: list[float]) -> tuple[float, str]: + """ + Non-parametric effect size, derived from the Mann-Whitney U statistic + (delta = 2U / (n1 n2) - 1) to stay O(n log n) instead of comparing every pair. + + Thresholds are Romano et al.'s conventional ones. + """ + n1, n2 = len(a), len(b) + if n1 == 0 or n2 == 0: + return float("nan"), "n/a" + u = stats.mannwhitneyu(a, b, alternative="two-sided").statistic + delta = float(2 * u / (n1 * n2) - 1) + m = abs(delta) + if m < 0.147: + label = "negligible" + elif m < 0.33: + label = "small" + elif m < 0.474: + label = "medium" + else: + label = "large" + return delta, label + + +@dataclass +class MetricStats: + metric: str + unit: str + n_with: int + n_without: int + mean_with: float + mean_without: float + median_with: float + median_without: float + std_with: float + std_without: float + delta_pct: float + welch_p: float + welch_sig: bool + d: float + d_label: str + mw_p: float + mw_sig: bool + cliff: float + cliff_label: str + normal_with: bool + normal_without: bool + + +def shapiro_normal(values: list[float]) -> bool: + """Shapiro-Wilk normality check, subsampled above 5000 points as scipy advises.""" + arr = np.asarray(values, dtype=float) + if len(arr) < 3 or np.ptp(arr) == 0: + return False + if len(arr) > 5000: + arr = np.random.default_rng(seed=42).choice(arr, size=5000, replace=False) + return bool(stats.shapiro(arr).pvalue > ALPHA) + + +def analyse_metric( + df_with: pd.DataFrame, df_without: pd.DataFrame, metric: str, unit: str +) -> MetricStats | None: + if metric not in df_with.columns or metric not in df_without.columns: + return None + + a = remove_outliers_zscore(df_with[metric].dropna().tolist()) + b = remove_outliers_zscore(df_without[metric].dropna().tolist()) + if len(a) < 2 or len(b) < 2: + return None + + arr_a, arr_b = np.asarray(a), np.asarray(b) + mean_a, mean_b = float(np.mean(arr_a)), float(np.mean(arr_b)) + + if np.ptp(arr_a) == 0 or np.ptp(arr_b) == 0: + welch_p, welch_sig = 1.0, False + else: + welch_p = float(stats.ttest_ind(a, b, equal_var=False).pvalue) + welch_sig = welch_p < ALPHA + + mw_p = float(stats.mannwhitneyu(a, b, alternative="two-sided").pvalue) + cliff, cliff_label = cliffs_delta(a, b) + d = cohens_d(a, b) + + # Same convention as EnergyTracer: positive means the smell costs more. + delta = (mean_a - mean_b) / mean_a * 100 if mean_a != 0 else float("nan") + + return MetricStats( + metric=metric, + unit=unit, + n_with=len(a), + n_without=len(b), + mean_with=mean_a, + mean_without=mean_b, + median_with=float(np.median(arr_a)), + median_without=float(np.median(arr_b)), + std_with=float(np.std(arr_a, ddof=1)), + std_without=float(np.std(arr_b, ddof=1)), + delta_pct=delta, + welch_p=welch_p, + welch_sig=welch_sig, + d=d, + d_label=effect_label(d), + mw_p=mw_p, + mw_sig=mw_p < ALPHA, + cliff=cliff, + cliff_label=cliff_label, + normal_with=shapiro_normal(a), + normal_without=shapiro_normal(b), + ) + + +# ── Discovery ───────────────────────────────────────────────────────────────── + + +@dataclass +class Shard: + size: int + profiler: str + phase: str + variant: str # "with" | "without" + path: Path + + +# Size directories are matched on the "-n" suffix only, not on a fixed +# prefix: campaigns archived under an older naming scheme stay readable, and a +# future rename of the harness will not silently make past data invisible. +SIZE_RE = re.compile(r"-n(\d+)$") +PHASE_RE = re.compile(r"^phase-(\d+)$") + +# Filled by discover(): size -> directory that holds it, so the ET-analyzer +# reports can be located later whatever the prefix happens to be. +SIZE_DIRS: dict[int, Path] = {} + + +def discover(out_tmp: Path, data_type: str) -> list[Shard]: + """ + Walk out-tmp and index every per-phase CSV. + + Tolerates the older layout that had no `phase-` level, so data collected + before that fix can still be salvaged (it is reported as phase "single"). + """ + shards: list[Shard] = [] + SIZE_DIRS.clear() + for size_dir in sorted(out_tmp.glob("*-n*")): + m = SIZE_RE.search(size_dir.name) + if not m or not size_dir.is_dir(): + continue + if size_dir.name.startswith("warmup"): + continue # warm-up shards are not measurements + size = int(m.group(1)) + SIZE_DIRS[size] = size_dir + + for csv_path in size_dir.rglob(f"{data_type}/csv/history_*_smell.csv"): + stem = csv_path.stem + variant = "without" if "without_smell" in stem else "with" + + rel = csv_path.relative_to(size_dir).parts + profiler = rel[0] if rel else "unknown" + phase = next( + (p for p in rel if PHASE_RE.match(p)), + "single", + ) + shards.append(Shard(size, profiler, phase, variant, csv_path)) + return shards + + +def load(shards: list[Shard]) -> pd.DataFrame: + frames = [] + for s in shards: + try: + df = pd.read_csv(s.path) + except Exception as exc: # noqa: BLE001 + print(f" skipped {s.path}: {exc}", file=sys.stderr) + continue + if df.empty: + continue + df["size"] = s.size + df["profiler"] = s.profiler + df["phase"] = s.phase + df["variant"] = s.variant + frames.append(df) + if not frames: + return pd.DataFrame() + return pd.concat(frames, ignore_index=True) + + +# ── Plots ───────────────────────────────────────────────────────────────────── + + +def unit_for(metric: str, profiler: str) -> str: + if metric == "time_s": + return "s" + if metric == "ane_mj" and profiler == "carbon": + return "mg CO2eq" + return "mJ" + + +def display_name(metric: str, profiler: str) -> str: + if metric == "ane_mj" and profiler == "carbon": + return "co2_eq" + return metric + + +def plot_distributions( + df_with: pd.DataFrame, df_without: pd.DataFrame, metric: str, + profiler: str, out_dir: Path, size: int, +) -> list[Path]: + a = remove_outliers_zscore(df_with[metric].dropna().tolist()) + b = remove_outliers_zscore(df_without[metric].dropna().tolist()) + if len(a) < 2 or len(b) < 2: + return [] + + unit = unit_for(metric, profiler) + name = display_name(metric, profiler) + written = [] + + fig, ax = plt.subplots(figsize=FIGURE_SIZE) + ax.boxplot([a, b], tick_labels=[LABEL_WITH, LABEL_WITHOUT], showmeans=True) + ax.set_ylabel(f"{name} ({unit})") + ax.set_title(f"{name} - distribution, N={size} (all phases, {len(a)}+{len(b)} samples)") + ax.grid(True, alpha=0.3) + p = out_dir / f"{name}_box.png" + fig.savefig(p, dpi=FIGURE_DPI, bbox_inches="tight") + plt.close(fig) + written.append(p) + + fig, ax = plt.subplots(figsize=FIGURE_SIZE) + parts = ax.violinplot([a, b], positions=[1, 2], showmeans=True, showmedians=True) + for body in parts["bodies"]: + body.set_facecolor("lightblue") + body.set_alpha(0.7) + body.set_edgecolor("black") + ax.set_xticks([1, 2]) + ax.set_xticklabels([LABEL_WITH, LABEL_WITHOUT]) + ax.set_ylabel(f"{name} ({unit})") + ax.set_title(f"{name} - density, N={size} (all phases)") + ax.grid(True, alpha=0.3) + p = out_dir / f"{name}_violin.png" + fig.savefig(p, dpi=FIGURE_DPI, bbox_inches="tight") + plt.close(fig) + written.append(p) + + return written + + +def plot_phase_drift( + df: pd.DataFrame, metric: str, profiler: str, out_dir: Path, size: int +) -> Path | None: + """ + Mean per phase. Flat lines mean the campaign was thermally stable; a trend means + the environment drifted and the campaign should be rerun before publishing. + """ + if "phase" not in df.columns or df["phase"].nunique() < 2: + return None + + def phase_key(p: str) -> int: + m = PHASE_RE.match(p) + return int(m.group(1)) if m else 0 + + phases = sorted(df["phase"].unique(), key=phase_key) + unit = unit_for(metric, profiler) + name = display_name(metric, profiler) + + fig, ax = plt.subplots(figsize=FIGURE_SIZE) + for variant, label in (("with", LABEL_WITH), ("without", LABEL_WITHOUT)): + means = [] + for ph in phases: + vals = df[(df["variant"] == variant) & (df["phase"] == ph)][metric].dropna() + means.append(np.mean(remove_outliers_zscore(vals.tolist())) if len(vals) else np.nan) + ax.plot(range(1, len(phases) + 1), means, marker="o", label=label) + + # Phases are integers: let matplotlib interpolate ticks and you get "1.25". + ax.set_xticks(range(1, len(phases) + 1)) + ax.set_xlabel("Measurement phase") + ax.set_ylabel(f"mean {name} ({unit})") + ax.set_title(f"{name} - stability across phases, N={size}") + ax.legend(fontsize="small") + ax.grid(True, alpha=0.3) + p = out_dir / f"{name}_per_phase.png" + fig.savefig(p, dpi=FIGURE_DPI, bbox_inches="tight") + plt.close(fig) + return p + + +def plot_scaling( + per_size: dict[int, list[MetricStats]], out_dir: Path +) -> Path | None: + """ + Overhead versus collection size. This is the strongest argument available: + an effect that grows with N establishes the causal mechanism, not just a correlation. + """ + sizes = sorted(per_size) + if len(sizes) < 2: + return None + + metrics = [m for m in ("cpu_mj", "dram_mj", "time_s") if any( + any(s.metric == m for s in per_size[sz]) for sz in sizes + )] + if not metrics: + return None + + fig, ax = plt.subplots(figsize=FIGURE_SIZE) + for metric in metrics: + ys = [] + for sz in sizes: + st = next((s for s in per_size[sz] if s.metric == metric), None) + ys.append(st.delta_pct if st else np.nan) + ax.plot(range(len(sizes)), ys, marker="o", label=metric) + + ax.axhline(0, color="black", linewidth=0.8) + ax.set_xticks(range(len(sizes))) + ax.set_xticklabels([str(s) for s in sizes]) + ax.set_xlabel("Collection size (children per parent)") + ax.set_ylabel("Overhead of EAGER over LAZY (%)") + ax.set_title("Does the effect grow with collection size?") + ax.legend(fontsize="small") + ax.grid(True, alpha=0.3) + p = out_dir / "scaling_overhead.png" + fig.savefig(p, dpi=FIGURE_DPI, bbox_inches="tight") + plt.close(fig) + return p + + +# ── Markdown assembly ───────────────────────────────────────────────────────── + + +def fmt_p(p: float) -> str: + if np.isnan(p): + return "n/a" + return f"{p:.2e}" if p < 0.001 else f"{p:.4f}" + + +def stats_table(rows: list[MetricStats]) -> list[str]: + out = [ + "| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict |", + "|---|---:|---:|---:|---:|---:|---:|---:|:---:|", + ] + for s in rows: + both = s.welch_sig and s.mw_sig + if both and abs(s.cliff) >= 0.147: + verdict = "significant" + elif both: + verdict = "significant but negligible" + elif s.welch_sig or s.mw_sig: + verdict = "borderline" + else: + verdict = "not significant" + out.append( + f"| `{s.metric}` | {s.mean_with:.4f} {s.unit} | {s.mean_without:.4f} {s.unit} " + f"| {s.delta_pct:+.2f}% | {fmt_p(s.welch_p)} | {s.d:+.3f} ({s.d_label}) " + f"| {fmt_p(s.mw_p)} | {s.cliff:+.3f} ({s.cliff_label}) | {verdict} |" + ) + return out + + +def build_markdown( + per_size: dict[int, list[MetricStats]], + sample_counts: dict[int, tuple[int, int, int]], + profiler: str, + data_type: str, + source_reports: dict[int, list[Path]], + scaling_plot: Path | None, + plots_by_size: dict[int, list[Path]], +) -> str: + L: list[str] = [] + L.append("# FetchType LAZY on JPA collections - consolidated energy report") + L.append("") + L.append( + "Aggregated across every measurement phase of the campaign. " + "Regenerated by `4-aggregate_report.py`; EnergyTracer's own per-size reports are " + "reproduced verbatim at the end." + ) + L.append("") + L.append("| | |") + L.append("|---|---|") + L.append(f"| Profiler | `{profiler}` |") + L.append(f"| Data type | `{data_type}` |") + L.append(f"| Collection sizes | {', '.join(str(s) for s in sorted(per_size))} |") + L.append(f"| Significance level | Ξ± = {ALPHA} |") + L.append(f"| Host | `{platform.node()}` - {platform.system()} {platform.release()} - {platform.machine()} |") + L.append("") + + L.append("## Sample sizes") + L.append("") + L.append("| Collection size | Phases | Samples EAGER | Samples LAZY |") + L.append("|---:|---:|---:|---:|") + for sz in sorted(sample_counts): + ph, nw, nwo = sample_counts[sz] + L.append(f"| {sz} | {ph} | {nw} | {nwo} |") + L.append("") + thin = [sz for sz, (_, nw, nwo) in sample_counts.items() + if min(nw, nwo) < MIN_SAMPLE_SIZE] + if thin: + L.append( + f"> ⚠ Sizes {', '.join(map(str, sorted(thin)))} have fewer than " + f"{MIN_SAMPLE_SIZE} samples per variant, below the minimum EnergyTracer's " + "own methodology considers acceptable. Treat these rows as exploratory." + ) + L.append("") + + if scaling_plot is not None: + L.append("## Effect versus collection size") + L.append("") + L.append( + "An overhead that grows with the number of children is the core argument " + "for the rule: it shows the cost is caused by hydrating the collection, " + "not by incidental noise." + ) + L.append("") + L.append(f"![scaling](plots/{scaling_plot.name})") + L.append("") + L.append("| Collection size | Ξ” cpu_mj | Ξ” dram_mj | Ξ” time_s |") + L.append("|---:|---:|---:|---:|") + for sz in sorted(per_size): + def d(m: str) -> str: + st = next((s for s in per_size[sz] if s.metric == m), None) + return f"{st.delta_pct:+.2f}%" if st else "n/a" + L.append(f"| {sz} | {d('cpu_mj')} | {d('dram_mj')} | {d('time_s')} |") + L.append("") + + for sz in sorted(per_size): + L.append(f"## Collection size N = {sz}") + L.append("") + L.extend(stats_table(per_size[sz])) + L.append("") + L.append( + "> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: " + "positive means the smell costs more." + ) + L.append("") + non_normal = [s.metric for s in per_size[sz] + if not (s.normal_with and s.normal_without)] + if non_normal: + L.append( + f"> Shapiro-Wilk rejects normality for {', '.join(f'`{m}`' for m in non_normal)}. " + "For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight " + "than the Welch t-test and Cohen's d." + ) + L.append("") + for p in plots_by_size.get(sz, []): + L.append(f"![{p.stem}](plots/n{sz}/{p.name})") + L.append("") + + if any(source_reports.values()): + L.append("## EnergyTracer reports, verbatim") + L.append("") + L.append( + "Unmodified output of `ET-analyzer`, kept so every number above can be " + "traced back to the tool itself." + ) + L.append("") + for sz in sorted(source_reports): + for rep in source_reports[sz]: + L.append(f"

N = {sz} - {rep.name}") + L.append("") + try: + L.append(rep.read_text(encoding="utf-8")) + except Exception as exc: # noqa: BLE001 + L.append(f"*(unreadable: {exc})*") + L.append("") + L.append("
") + L.append("") + + L.append("## How to read this, and what not to claim") + L.append("") + L.append( + "- A result is worth quoting only when Welch **and** Mann-Whitney agree and " + "Cliff's Ξ΄ is at least small. Large sample sizes make tiny differences " + "\"significant\" without making them relevant." + ) + L.append( + "- Check the per-phase plots before trusting anything: a visible trend means " + "the machine drifted during the campaign and the run should be repeated." + ) + L.append( + "- These figures measure the client side only - no database engine, no network. " + "They are a lower bound on the real saving." + ) + L.append("") + return "\n".join(L) + + +# ── Main ────────────────────────────────────────────────────────────────────── + + +def main() -> int: + here = Path(__file__).resolve().parent + ap = argparse.ArgumentParser( + description="Aggregate a full EnergyTracer campaign into one report directory." + ) + ap.add_argument("--out-tmp", type=Path, default=here / "out-tmp", + help="campaign scratch directory (default: ./out-tmp)") + ap.add_argument("--output", type=Path, default=here / "consolidated-report", + help="report directory to create (default: ./consolidated-report)") + ap.add_argument("--data-type", choices=["raw", "cleaned"], default="raw", + help="which EnergyTracer data set to aggregate. 'raw' is " + "recommended: it is filtered once, globally, whereas " + "'cleaned' was already filtered per phase on small samples " + "(default: raw)") + args = ap.parse_args() + + out_tmp: Path = args.out_tmp.resolve() + output: Path = args.output.resolve() + + if not out_tmp.is_dir(): + print(f"error: {out_tmp} does not exist - run a campaign first", file=sys.stderr) + return 1 + + print(f"Scanning {out_tmp} for '{args.data_type}' data…") + shards = discover(out_tmp, args.data_type) + if not shards: + print(f"error: no '{args.data_type}' CSV found under {out_tmp}", file=sys.stderr) + print("Expected .../-n//phase-/" + f"{args.data_type}/csv/history_*_smell.csv", file=sys.stderr) + return 1 + + df = load(shards) + if df.empty: + print("error: every CSV was empty or unreadable", file=sys.stderr) + return 1 + + profilers = sorted(df["profiler"].unique()) + if len(profilers) > 1: + print(f"warning: several profilers present ({', '.join(profilers)}); " + "reporting them together is not meaningful", file=sys.stderr) + profiler = profilers[0] + + if output.exists(): + shutil.rmtree(output) + (output / "data").mkdir(parents=True) + (output / "plots").mkdir(parents=True) + (output / "reports-source").mkdir(parents=True) + + per_size: dict[int, list[MetricStats]] = {} + sample_counts: dict[int, tuple[int, int, int]] = {} + plots_by_size: dict[int, list[Path]] = {} + source_reports: dict[int, list[Path]] = {} + + for size in sorted(df["size"].unique()): + sub = df[df["size"] == size] + d_with = sub[sub["variant"] == "with"] + d_without = sub[sub["variant"] == "without"] + n_phases = sub["phase"].nunique() + sample_counts[int(size)] = (n_phases, len(d_with), len(d_without)) + print(f" N={size}: {n_phases} phases, " + f"{len(d_with)} + {len(d_without)} samples") + + size_data = output / "data" / f"n{size}" + size_data.mkdir(parents=True, exist_ok=True) + d_with.to_csv(size_data / "with_smell.csv", index=False) + d_without.to_csv(size_data / "without_smell.csv", index=False) + + rows = [] + for metric in METRICS: + st = analyse_metric(d_with, d_without, metric, unit_for(metric, profiler)) + if st: + rows.append(st) + per_size[int(size)] = rows + + size_plots = output / "plots" / f"n{size}" + size_plots.mkdir(parents=True, exist_ok=True) + written: list[Path] = [] + for metric in METRICS: + written += plot_distributions( + d_with, d_without, metric, profiler, size_plots, int(size) + ) + drift = plot_phase_drift(sub, metric, profiler, size_plots, int(size)) + if drift: + written.append(drift) + plots_by_size[int(size)] = written + + # EnergyTracer's own reports, copied so nothing is lost. + found: list[Path] = [] + results_dir = SIZE_DIRS[int(size)] / "results" + if results_dir.is_dir(): + dest = output / "reports-source" / f"n{size}" + dest.mkdir(parents=True, exist_ok=True) + for md in sorted(results_dir.rglob("*_report.md")): + target = dest / f"{md.parent.parent.name}_{md.name}" + shutil.copy2(md, target) + found.append(target) + source_reports[int(size)] = found + + df.to_csv(output / "data" / "all_measurements.csv", index=False) + scaling = plot_scaling(per_size, output / "plots") + + (output / "REPORT.md").write_text( + build_markdown(per_size, sample_counts, profiler, args.data_type, + source_reports, scaling, plots_by_size), + encoding="utf-8", + ) + + n_plots = sum(len(v) for v in plots_by_size.values()) + (1 if scaling else 0) + print() + print(f"Report written to {output}") + print(f" REPORT.md consolidated document") + print(f" data/ {len(df)} measurements, per size + combined") + print(f" plots/ {n_plots} figures regenerated from the full sample") + print(f" reports-source/ {sum(len(v) for v in source_reports.values())} " + "EnergyTracer reports, unmodified") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/4-aggregate_report.sh b/src/main/rules/S6904/java/analysis/level2-energytracer/4-aggregate_report.sh new file mode 100755 index 000000000..66676b683 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/4-aggregate_report.sh @@ -0,0 +1,55 @@ +#!/usr/bin/env bash +# Builds the consolidated campaign report from out-tmp/. +# +# Usage: +# ./4-aggregate_report.sh [path-to-EnergyTracer-checkout] [-- extra args] +# +# Examples: +# ./4-aggregate_report.sh ~/git_creedengo2/EnergyTracer +# ./4-aggregate_report.sh ~/git_creedengo2/EnergyTracer -- --data-type cleaned +# ./4-aggregate_report.sh # uses the system python3 +# +# Output: consolidated-report/ - REPORT.md, aggregated CSVs, regenerated plots, and +# EnergyTracer's own reports kept verbatim. See ../README.md section 6. +# +# To rebuild the report of an archived campaign, point it at that campaign: +# ./4-aggregate_report.sh ~/git_creedengo2/EnergyTracer -- \ +# --out-tmp campaign1/out-tmp --output campaign1/consolidated-report +# +# Passing the EnergyTracer checkout is the easy path: its virtualenv already has +# pandas, numpy, scipy and matplotlib, so nothing needs installing. Without it, +# the script falls back to python3 and those four packages must be importable. +set -euo pipefail + +HERE="$(cd "$(dirname "$0")" && pwd)" +PY="$HERE/4-aggregate_report.py" + +ET_DIR="" +if [ $# -gt 0 ] && [ "${1:-}" != "--" ]; then + ET_DIR="$1" + shift +fi +[ "${1:-}" = "--" ] && shift + +if [ -n "$ET_DIR" ]; then + if [ ! -d "$ET_DIR" ]; then + echo "not a directory: $ET_DIR" >&2 + exit 1 + fi + ET_DIR="$(cd "$ET_DIR" && pwd)" + command -v uv >/dev/null || { echo "uv not found in PATH" >&2; exit 1; } + exec uv run --project "$ET_DIR" python "$PY" "$@" +fi + +command -v python3 >/dev/null || { echo "python3 not found" >&2; exit 1; } +if ! python3 -c "import pandas, numpy, scipy, matplotlib" 2>/dev/null; then + cat >&2 <<'EOF' +Missing Python dependencies (pandas, numpy, scipy, matplotlib). + +Easiest fix - reuse the EnergyTracer virtualenv, which already has them: + + ./4-aggregate_report.sh ~/git_creedengo2/EnergyTracer +EOF + exit 1 +fi +exec python3 "$PY" "$@" diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/campaign1-interpretation.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/campaign1-interpretation.md new file mode 100644 index 000000000..cd9ea558a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/campaign1-interpretation.md @@ -0,0 +1,306 @@ +# Interpretation of the measurements β€” rule "FetchType.LAZY on JPA collections" (S6904) + +*Analysis of the campaign run on 2026-08-04. Internal working document.* + +*Context note: SonarQube already ships a built-in rule under the identifier S6904, so the +four historical PRs this harness was built to support (#122, #125, #155, #325) are being +closed as duplicates rather than merged. This document is kept regardless, as independent +evidence that the underlying code smell has a real, measurable cost β€” useful to justify why +the built-in rule matters and to inform its configuration (severity, default scope), even +though no new rule will be shipped from this work.* + +--- + +## Summary + +**The rule's impact is measured, robust, and grows faster than collection size.** Over 30 +paired phases and 900 runs per variant per size, measured with the hardware counters of a +MacBook Pro M1 Pro, hydrating a collection that is never used costs +**0.31 to 0.92 Β΅J per child row**. All 30 phases point the same way at all three sizes β€” +p β‰ˆ 2Β·10⁻⁹ by a sign test, with no distributional assumption at all. + +**The percentage (81% to 91%) should not be the main argument**, for three reasons +developed in Β§ 5: it depends on a bench-calibration parameter, the absolute yearly saving +is modest, and at small sizes the JPA bench β€” more realistic β€” shows a much smaller gap +(x1.81 in time at N=10 versus x5.3 in energy). + +**The recommended case** rests on three pillars that are harder to contest: + +1. **Super-linearity.** Multiplying collection size by 10 multiplies energy per read by 12 + to 13, then by 21 to 23 β€” the cost grows as N^1.10 to N^1.37. +2. **Latency and memory**: x12.65 in time and x32 in allocation at N=1000, measured on a + real Hibernate stack. +3. **Zero fix cost**: one annotation attribute. + +**Two reservations not to hide.** First, the 900 runs are not independent (inter-variant +correlation per phase of 0.70 to 0.95): the valid statistical unit is the phase, n=30, and +the tool's astronomical p-values are indefensible. Second, part of the super-linearity may +come from the simulated persistence context, which retains 1,024 parents, so its memory +footprint grows with N. + +**Conclusion**: the rule is clearly relevant for collections on the order of a hundred +elements and above; the gain is marginal at ten. + +--- + +## 1. What was measured + +| | Level 1 β€” JPA | Level 2 β€” energy | +|---|---|---| +| Stack | Hibernate 6.4.4 + H2, JDK 21.0.11 | Plain Java, simulated row source | +| Tool | Hibernate Statistics + JDK | EnergyTracer, `mac` profiler (hardware counters) | +| Machine | MacBook Pro M1 Pro (arm64) | MacBookPro18,1 β€” Apple M1 Pro | +| Volume | **A single run**, 100 to 2,000 reads depending on N | 30 phases Γ— 30 iterations = 900 runs per variant | +| Protocol | β€” | 60 s cooldown, randomised variant order, machine prepared | + +Scenario identical at both levels, and strictly the one the rule targets: a parent entity +owns a collection of children, the application loads the parent and **never accesses** +the collection. Sizes: **10, 100 and 1000 children per parent**. + +**Property of the energy bench only**: the number of child rows hydrated per run is held +constant at 5,000,000 there (500,000 Γ— 10, 50,000 Γ— 100, 5,000 Γ— 1000), which makes the +three sizes comparable at equal nominal work. The JPA bench, by contrast, performs +20,000, 20,000 and 100,000 hydrations β€” its ratios should be read per read, not in +absolute terms. + +**Scope of the energy totals**: `ane_mj` is **excluded from every calculation** in this +document (see Β§ 4). Totals therefore equal CPU + GPU + DRAM. + +--- + +## 2. Results + +### Level 1 β€” functional cost (real Hibernate) + +| N | Statements / read | Hydrated entities / read | Time | Allocation | +|---:|---:|---:|---:|---:| +| 10 | 1.00 β†’ 1.00 | 11 β†’ 1 | x1.81 | x1.38 | +| 100 | 1.00 β†’ 1.00 | 101 β†’ 1 | x3.73 | x4.37 | +| 1000 | 1.00 β†’ 1.00 | 1001 β†’ 1 | x12.65 | x32.09 | + +**The statement count is identical β€” 1.00 per read in every case.** An important and +counter-intuitive result: Hibernate resolves the eager collection with a join, with no +extra round trip. **The overhead is therefore not an N+1 problem** but a payload and +hydration problem. This confirms the scope decision from the fusion proposal: N+1 on +single-valued associations belongs to a separate rule. + +LAZY allocation is flat (β‰ˆ 24 KiB per read regardless of N), EAGER allocation grows from +33.6 to 764.1 KiB. + +*Caveat: this level rests on a single run, and only 100 reads at N=1000. The ratios are +orders of magnitude, not repeated measurements.* + +### Level 2 β€” energy cost + +Means over 900 runs, outliers removed at 3Οƒ, CPU + GPU + DRAM: + +| N | EAGER | LAZY | Gap | Relative gap | Ratio | +|---:|---:|---:|---:|---:|---:| +| 10 | 2,047.7 mJ | 388.4 mJ | 1,659.3 mJ | +81.0% | x5.27 | +| 100 | 2,510.6 mJ | 343.5 mJ | 2,167.1 mJ | +86.3% | x7.31 | +| 1000 | 5,041.9 mJ | 468.0 mJ | 4,573.9 mJ | +90.7% | x10.77 | + +### The correct statistic: at the phase level + +The 900 runs within one phase share the same thermal state and the same system context. +They are not independent: the correlation between a phase's EAGER and LAZY means is +**+0.695 at N=10, +0.851 at N=100, +0.945 at N=1000**. Treating the 900 runs as 900 +independent observations β€” which is what the tool does β€” artificially inflates +significance. **The valid unit is the phase: n = 30 pairs.** + +| N | Mean gap per phase | 95% CI | Relative gap | Phases pointing the same way | +|---:|---:|---:|---:|:---:| +| 10 | 1,674.9 mJ | [1,564.7, 1,785.1] | 80.83% Β± 0.76 | **30 / 30** | +| 100 | 2,166.3 mJ | [2,028.9, 2,303.7] | 86.29% Β± 0.52 | **30 / 30** | +| 1000 | 4,637.2 mJ | [4,545.2, 4,729.2] | 90.61% Β± 0.20 | **30 / 30** | + +All 30 phases point the same way at all three sizes. **Sign test: p = 2 Γ— 0.5³⁰ β‰ˆ +1.9 Γ— 10⁻⁹**, with no assumption of normality, equal variance, or within-phase +independence. This is the figure to cite, and it is more than enough. + +Beyond that, on `cpu_mj`, `dram_mj` and `time_s`, the distributions are **strictly +disjoint** at all three sizes: at N=1000 for instance, the LAZY maximum is 542.9 mJ +against an EAGER minimum of 4,265.2 mJ. No statistical test is needed to settle it. + +*Nuance: this disjointness does not hold for `gpu_mj`, where the distributions overlap at +N=10 and N=100. The GPU accounts for under 0.15% of the gap anyway and is not an +argument.* + +Breakdown of the gap by component: + +| N | CPU | DRAM | GPU | +|---:|---:|---:|---:| +| 10 | 76.1% | 23.8% | 0.1% | +| 100 | 79.9% | 20.0% | 0.1% | +| 1000 | 86.9% | 13.0% | 0.0% | + +The gap is carried by the CPU (object creation, decoding, collection insert) and by DRAM +(memory pressure). The CPU share grows with size. + +--- + +## 3. The figure to publish: energy per hydrated row + +| N | Β΅J / hydrated row | Range depending on treatment | Β΅J / parent read | +|---:|---:|---:|---:| +| 10 | 0.33 | 0.31 – 0.34 | 3.1 – 3.3 | +| 100 | 0.43 | 0.39 – 0.44 | 39 – 44 | +| 1000 | 0.92 | 0.92 – 0.93 | β‰ˆ 915 | + +The range reflects including or excluding the high-regime phases (Β§ 4). **Publish two +significant figures**: the third decimal is not supported by the data. + +### Super-linearity, the central argument + +| Step | Rows | Energy / read | Exponent | +|---|---:|---:|---:| +| N=10 β†’ 100 | x10 | x12.4 to x13.1 | 1.10 – 1.12 | +| N=100 β†’ 1000 | x10 | x20.9 to x23.5 | 1.32 – 1.37 | + +Multiplying the collection by 10 multiplies the energy cost per read by 12-13, then by +21-23. The cost grows as **N^1.10 to N^1.37**, not linearly β€” and the conclusion holds +under both data treatments, which is the important point. Each individual row also gets +more expensive as the collection grows: 0.33 Β΅J at N=10 versus 0.92 Β΅J at N=1000. + +**Honest reservation about the mechanism.** Two explanations overlap and this +measurement does not separate them: + +- degrading cache locality and rising garbage-collector pressure as collections grow β€” + the effect the rule targets; +- the **bench's simulated persistence context**, which retains 1,024 parents + (`CONTEXT_SIZE`): under EAGER, the live set therefore holds 1,024 Γ— N child objects, + growing mechanically with N. No `-Xmx` was fixed. + +This second factor is defensible β€” a real persistence context does retain hydrated +collections β€” but it means part of the super-linearity reflects live-set size rather than +a per-row cost. **A sensitivity study on `CONTEXT_SIZE` and `-Xmx` would resolve the +ambiguity**; in its absence, present the super-linearity as observed on this bench, not +as a general law. + +--- + +## 4. Quality control: two regimes, not a drift + +The per-phase stability plot initially suggested thermal drift. Examining the affected +phases shows something else: a **binary factor**. + +| N | High-regime phases | Gap over the low regime | +|---:|---|---:| +| 10 | 22, 28, 29, 30 (4/30) | +48% | +| 100 | 1, 2, 7, 8, 26, 27, 28, 29 (8/30) | +41% | +| 1000 | 29, 30 (2/30) | +23% | + +At N=100, the high phases are **1, 2, 7, 8** then **26 to 29**: non-contiguous, and +including the first ones. Gradual heating does not produce this pattern. The plausible +hypothesis is a scheduling shift (performance cores versus efficiency cores) or +intermittent system activity. The bimodality is visible on the violin plots. + +**Why the conclusion holds.** The factor affects both variants together, since they are +measured within the same phase and in randomised order. The relative gap is therefore +insensitive to it: + +| N | EAGER drift | LAZY drift | Ξ”% phases 1-5 β†’ 26-30 | +|---:|---:|---:|---:| +| 10 | +25.2% | +25.1% | 81.19 β†’ 81.10 (βˆ’0.09 pt) | +| 100 | +0.8% | βˆ’1.8% | 86.16 β†’ 86.01 (βˆ’0.16 pt) | +| 1000 | +8.4% | +15.3% | 90.67 β†’ 90.05 (βˆ’0.62 pt) | + +The relative gap never moves by more than 0.7 points. And energy per row, recomputed +excluding the high phases, varies from βˆ’1.4% to βˆ’10% β€” hence the range in Β§ 3. + +This is the strength of the paired, randomised design: it absorbs perturbations that hit +both variants at once. Worth keeping in mind when reading the plots. + +### Anomaly excluded: `ane_mj` + +The ANE (Apple Neural Engine) counter reports 11.4 mJ at N=10, 23.9 mJ at N=100 and +0.0012 mJ at N=1000 β€” with no neural workload in the benchmark, and no plausible +mechanism for such variation across sizes. A measurement artefact, or crosstalk with +system activity. + +Its Cliff's Ξ΄ is negligible everywhere (+0.050, +0.121, +0.036) and it accounts for under +1% of the total. **It is excluded from every figure in this document.** This lowers +totals by roughly 0.6% at N=10 and N=100, without changing any conclusion. + +### Average power + +At N=10, the LAZY variant shows a *higher* average power draw (5.17 W versus 4.18 W): it +works in short bursts. **The saving comes from duration, not from a lower instantaneous +draw.** At N=1000, EAGER reaches 7.18 W against 3.54 W β€” sustained memory pressure. EAGER +power grows from 4.18 to 7.18 W with size, consistent with the super-linear reading. + +--- + +## 5. What not to claim + +### "EAGER consumes 91% more energy" + +Wrong out of context. At level 2, the LAZY variant does almost nothing: the gap measures +the share of hydration **within this synthetic program**, a share that depends on the +`PARENT_READS` parameter chosen to calibrate the bench. In production this cost is +diluted by SQL, network, serialisation and business logic. + +Correct wording: "hydrating an unused collection costs 0.33 to 0.92 Β΅J per row, and that +cost grows faster than the number of rows". + +### The tool's p-values + +`ET-analyzer` reports p-values below 10⁻²⁷⁰, computed on 900 samples treated as +independent. They are not (Β§ 2). **Do not cite them**: a statistically literate reviewer +will have no trouble dismantling the reasoning, and that would be a shame since the +result does not need it. Cite the sign test over 30 phases (p β‰ˆ 2Β·10⁻⁹) and the +disjointness of the distributions instead. + +### The two levels do not contradict each other + +At N=10, level 1 reports x1.81 in time while level 2 reports x5.27 in energy. This is not +an inconsistency: at level 1, a full LAZY read costs β‰ˆ 67 Β΅s (session open, query, parent +hydration, close), which dwarfs the cost of hydrating 10 rows. Level 2 has a much leaner +baseline. + +**Level 1 is the realistic figure at small sizes.** The two converge at N=1000: x12.65 in +time versus x10.77 in energy. + +### The absolute saving is modest + +A service handling 1,000 requests per second, each loading a parent with 100 unused +children, wastes: + +> 1,000 Γ— 100 Γ— 0.43 Β΅J β‰ˆ **43 mW**, roughly **0.38 kWh per year** + +That is little β€” a few hours of laptop use. And it is a lower bound: the database-server +cost and the network transfer are not measured. + +**Defending the rule on energy savings alone would be fragile.** It is defended by the +combination: latency divided by 12, allocation by 32, super-linear growth, free fix. The +energy figure confirms the mechanism; it is not the knockout argument. + +### The relevance threshold is not resolved + +No size between 10 and 100 was tested. Saying "from about a hundred" is an +interpolation, not a measurement. Honest wording: the gain is marginal at ten, clear at a +hundred, and the crossover point was not sought. + +--- + +## Sources + +- Consolidated report: `level2-energytracer/campaign1/consolidated-report/REPORT.md` +- Consolidated data: `level2-energytracer/campaign1/consolidated-report/data/` β€” 5,400 measurements (3 sizes Γ— 2 variants Γ— 900) +- Plots: `level2-energytracer/campaign1/consolidated-report/plots/` +- Raw EnergyTracer reports: `level2-energytracer/campaign1/consolidated-report/reports-source/` +- Level 1: `level1-jpa/results/level1-20260802-120143.txt` β€” file predates both the + harness rename and the S6904 assignment; its header still reads the original rule + identifier used before this repository location was settled. + +Every figure in this document was recomputed from the raw data, independently of the +tool's own report. + +See also `level2-energytracer/campaign2/campaign2-interpretation.md` for the follow-up +campaign, which adds three intermediate sizes (30, 50, 500) and refines this document's +conclusions on the scaling law and the relevance threshold. + +--- + +*Internal analysis, kept as evidence that the code smell targeted by S6904 has a real, +measurable cost.* diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/REPORT.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/REPORT.md new file mode 100644 index 000000000..ab7923fbd --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/REPORT.md @@ -0,0 +1,425 @@ +# FetchType LAZY on JPA collections - consolidated energy report + +Aggregated across every measurement phase of the campaign. Regenerated by `aggregate_report.py`; EnergyTracer's own per-size reports are reproduced verbatim at the end. + +| | | +|---|---| +| Profiler | `mac` | +| Data type | `raw` | +| Collection sizes | 10, 100, 1000 | +| Significance level | Ξ± = 0.05 | +| Host | `M-NGY9VPYVMH` - Darwin 25.5.0 - arm64 | + +## Sample sizes + +| Collection size | Phases | Samples EAGER | Samples LAZY | +|---:|---:|---:|---:| +| 10 | 30 | 900 | 900 | +| 100 | 30 | 900 | 900 | +| 1000 | 30 | 900 | 900 | + +## Effect versus collection size + +An overhead that grows with the number of children is the core argument for the rule: it shows the cost is caused by hydrating the collection, not by incidental noise. + +![scaling](plots/scaling_overhead.png) + +| Collection size | Ξ” cpu_mj | Ξ” dram_mj | Ξ” time_s | +|---:|---:|---:|---:| +| 10 | +79.74% | +85.45% | +84.95% | +| 100 | +86.12% | +87.13% | +84.73% | +| 1000 | +91.17% | +87.83% | +81.06% | + +## Collection size N = 10 + +| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict | +|---|---:|---:|---:|---:|---:|---:|---:|:---:| +| `cpu_mj` | 1583.1190 mJ | 320.7996 mJ | +79.74% | 0.00e+00 | +6.500 (large) | 7.58e-289 | +1.000 (large) | significant | +| `gpu_mj` | 2.0598 mJ | 0.2778 mJ | +86.51% | 1.12e-33 | +0.605 (medium) | 1.33e-149 | +0.705 (large) | significant | +| `ane_mj` | 11.3956 mJ | 1.6459 mJ | +85.56% | 9.85e-23 | +0.475 (small) | 0.0062 | +0.050 (negligible) | significant but negligible | +| `dram_mj` | 462.4862 mJ | 67.2775 mJ | +85.45% | 0.00e+00 | +28.600 (large) | 4.69e-287 | +1.000 (large) | significant | +| `time_s` | 0.4878 s | 0.0734 s | +84.95% | 0.00e+00 | +52.041 (large) | 6.69e-294 | +1.000 (large) | significant | + +> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: positive means the smell costs more. + +> Shapiro-Wilk rejects normality for `cpu_mj`, `gpu_mj`, `ane_mj`, `dram_mj`, `time_s`. For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight than the Welch t-test and Cohen's d. + +![cpu_mj_box](plots/n10/cpu_mj_box.png) +![cpu_mj_violin](plots/n10/cpu_mj_violin.png) +![cpu_mj_per_phase](plots/n10/cpu_mj_per_phase.png) +![gpu_mj_box](plots/n10/gpu_mj_box.png) +![gpu_mj_violin](plots/n10/gpu_mj_violin.png) +![gpu_mj_per_phase](plots/n10/gpu_mj_per_phase.png) +![ane_mj_box](plots/n10/ane_mj_box.png) +![ane_mj_violin](plots/n10/ane_mj_violin.png) +![ane_mj_per_phase](plots/n10/ane_mj_per_phase.png) +![dram_mj_box](plots/n10/dram_mj_box.png) +![dram_mj_violin](plots/n10/dram_mj_violin.png) +![dram_mj_per_phase](plots/n10/dram_mj_per_phase.png) +![time_s_box](plots/n10/time_s_box.png) +![time_s_violin](plots/n10/time_s_violin.png) +![time_s_per_phase](plots/n10/time_s_per_phase.png) + +## Collection size N = 100 + +| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict | +|---|---:|---:|---:|---:|---:|---:|---:|:---:| +| `cpu_mj` | 2011.7589 mJ | 279.2056 mJ | +86.12% | 0.00e+00 | +6.230 (large) | 2.25e-295 | +1.000 (large) | significant | +| `gpu_mj` | 1.9044 mJ | 0.3575 mJ | +81.23% | 4.98e-169 | +1.546 (large) | 4.75e-179 | +0.776 (large) | significant | +| `ane_mj` | 23.8978 mJ | 3.2133 mJ | +86.55% | 2.91e-48 | +0.729 (medium) | 1.09e-08 | +0.121 (negligible) | significant but negligible | +| `dram_mj` | 496.8967 mJ | 63.9467 mJ | +87.13% | 0.00e+00 | +20.514 (large) | 2.25e-295 | +1.000 (large) | significant | +| `time_s` | 0.5221 s | 0.0797 s | +84.73% | 0.00e+00 | +74.836 (large) | 2.91e-292 | +1.000 (large) | significant | + +> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: positive means the smell costs more. + +> Shapiro-Wilk rejects normality for `cpu_mj`, `gpu_mj`, `ane_mj`, `dram_mj`, `time_s`. For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight than the Welch t-test and Cohen's d. + +![cpu_mj_box](plots/n100/cpu_mj_box.png) +![cpu_mj_violin](plots/n100/cpu_mj_violin.png) +![cpu_mj_per_phase](plots/n100/cpu_mj_per_phase.png) +![gpu_mj_box](plots/n100/gpu_mj_box.png) +![gpu_mj_violin](plots/n100/gpu_mj_violin.png) +![gpu_mj_per_phase](plots/n100/gpu_mj_per_phase.png) +![ane_mj_box](plots/n100/ane_mj_box.png) +![ane_mj_violin](plots/n100/ane_mj_violin.png) +![ane_mj_per_phase](plots/n100/ane_mj_per_phase.png) +![dram_mj_box](plots/n100/dram_mj_box.png) +![dram_mj_violin](plots/n100/dram_mj_violin.png) +![dram_mj_per_phase](plots/n100/dram_mj_per_phase.png) +![time_s_box](plots/n100/time_s_box.png) +![time_s_violin](plots/n100/time_s_violin.png) +![time_s_per_phase](plots/n100/time_s_per_phase.png) + +## Collection size N = 1000 + +| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict | +|---|---:|---:|---:|---:|---:|---:|---:|:---:| +| `cpu_mj` | 4360.2893 mJ | 385.0175 mJ | +91.17% | 0.00e+00 | +89.445 (large) | 3.84e-276 | +1.000 (large) | significant | +| `gpu_mj` | 2.3046 mJ | 0.3733 mJ | +83.80% | 0.00e+00 | +3.683 (large) | 2.87e-296 | +1.000 (large) | significant | +| `ane_mj` | 0.0012 mJ | 0.0000 mJ | +100.00% | 1.0000 | +0.272 (small) | 3.30e-08 | +0.036 (negligible) | borderline | +| `dram_mj` | 679.3202 mJ | 82.6486 mJ | +87.83% | 0.00e+00 | +117.690 (large) | 2.82e-277 | +1.000 (large) | significant | +| `time_s` | 0.6704 s | 0.1270 s | +81.06% | 0.00e+00 | +134.006 (large) | 2.29e-288 | +1.000 (large) | significant | + +> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: positive means the smell costs more. + +> Shapiro-Wilk rejects normality for `cpu_mj`, `gpu_mj`, `ane_mj`, `dram_mj`, `time_s`. For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight than the Welch t-test and Cohen's d. + +![cpu_mj_box](plots/n1000/cpu_mj_box.png) +![cpu_mj_violin](plots/n1000/cpu_mj_violin.png) +![cpu_mj_per_phase](plots/n1000/cpu_mj_per_phase.png) +![gpu_mj_box](plots/n1000/gpu_mj_box.png) +![gpu_mj_violin](plots/n1000/gpu_mj_violin.png) +![gpu_mj_per_phase](plots/n1000/gpu_mj_per_phase.png) +![ane_mj_box](plots/n1000/ane_mj_box.png) +![ane_mj_violin](plots/n1000/ane_mj_violin.png) +![ane_mj_per_phase](plots/n1000/ane_mj_per_phase.png) +![dram_mj_box](plots/n1000/dram_mj_box.png) +![dram_mj_violin](plots/n1000/dram_mj_violin.png) +![dram_mj_per_phase](plots/n1000/dram_mj_per_phase.png) +![time_s_box](plots/n1000/time_s_box.png) +![time_s_violin](plots/n1000/time_s_violin.png) +![time_s_per_phase](plots/n1000/time_s_per_phase.png) + +## EnergyTracer reports, verbatim + +Unmodified output of `ET-analyzer`, kept so every number above can be traced back to the tool itself. + +
N = 10 - cleaned_mac_report.md + +## Energy Report β€” `mac` (cleaned) + +> 880 samples (with smell) vs 882 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 481.32 ms | 73.56 ms | +| **Average Power** | 4.194 W | 5.170 W | +| **Total Energy** | 1776.26 J | 335.44 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.68% | 0.00e+00 | +6.469 | large | βœ… | +| `gpu_mj` | +86.23% | 6.17e-33 | +0.604 | medium | βœ… | +| `ane_mj` | +85.06% | 3.92e-22 | +0.473 | small | βœ… | +| `dram_mj` | +85.42% | 0.00e+00 | +28.839 | large | βœ… | +| `time_s` | +84.91% | 0.00e+00 | +53.990 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.7% lower energy (Cohen’s d = +6.469, large) +- **`gpu_mj`**: 86.2% lower energy (Cohen’s d = +0.604, medium) +- **`ane_mj`**: 85.1% lower energy (Cohen’s d = +0.473, small) +- **`dram_mj`**: 85.4% lower energy (Cohen’s d = +28.839, large) +- **`time_s`**: 84.9% lower time (Cohen’s d = +53.990, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 10 - raw_mac_report.md + +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 482.89 ms | 73.43 ms | +| **Average Power** | 4.177 W | 5.169 W | +| **Total Energy** | 1815.47 J | 341.56 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.74% | 0.00e+00 | +6.500 | large | βœ… | +| `gpu_mj` | +86.51% | 1.12e-33 | +0.605 | medium | βœ… | +| `ane_mj` | +85.56% | 9.85e-23 | +0.475 | small | βœ… | +| `dram_mj` | +85.45% | 0.00e+00 | +28.600 | large | βœ… | +| `time_s` | +84.95% | 0.00e+00 | +52.041 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.7% lower energy (Cohen’s d = +6.500, large) +- **`gpu_mj`**: 86.5% lower energy (Cohen’s d = +0.605, medium) +- **`ane_mj`**: 85.6% lower energy (Cohen’s d = +0.475, small) +- **`dram_mj`**: 85.5% lower energy (Cohen’s d = +28.600, large) +- **`time_s`**: 84.9% lower time (Cohen’s d = +52.041, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 100 - cleaned_mac_report.md + +## Energy Report β€” `mac` (cleaned) + +> 891 samples (with smell) vs 875 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 521.44 ms | 78.82 ms | +| **Average Power** | 4.860 W | 4.414 W | +| **Total Energy** | 2258.08 J | 304.41 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.07% | 0.00e+00 | +6.228 | large | βœ… | +| `gpu_mj` | +80.99% | 2.11e-166 | +1.541 | large | βœ… | +| `ane_mj` | +86.38% | 1.05e-47 | +0.729 | medium | βœ… | +| `dram_mj` | +87.09% | 0.00e+00 | +20.546 | large | βœ… | +| `time_s` | +84.69% | 0.00e+00 | +75.359 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.1% lower energy (Cohen’s d = +6.228, large) +- **`gpu_mj`**: 81.0% lower energy (Cohen’s d = +1.541, large) +- **`ane_mj`**: 86.4% lower energy (Cohen’s d = +0.729, medium) +- **`dram_mj`**: 87.1% lower energy (Cohen’s d = +20.546, large) +- **`time_s`**: 84.7% lower time (Cohen’s d = +75.359, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 100 - raw_mac_report.md + +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 520.34 ms | 78.33 ms | +| **Average Power** | 4.871 W | 4.426 W | +| **Total Energy** | 2281.01 J | 312.04 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.12% | 0.00e+00 | +6.230 | large | βœ… | +| `gpu_mj` | +81.23% | 4.98e-169 | +1.546 | large | βœ… | +| `ane_mj` | +86.55% | 2.91e-48 | +0.729 | medium | βœ… | +| `dram_mj` | +87.13% | 0.00e+00 | +20.514 | large | βœ… | +| `time_s` | +84.73% | 0.00e+00 | +74.836 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.1% lower energy (Cohen’s d = +6.230, large) +- **`gpu_mj`**: 81.2% lower energy (Cohen’s d = +1.546, large) +- **`ane_mj`**: 86.6% lower energy (Cohen’s d = +0.729, medium) +- **`dram_mj`**: 87.1% lower energy (Cohen’s d = +20.514, large) +- **`time_s`**: 84.7% lower time (Cohen’s d = +74.836, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 1000 - cleaned_mac_report.md + +## Energy Report β€” `mac` (cleaned) + +> 892 samples (with smell) vs 874 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 650.62 ms | 125.11 ms | +| **Average Power** | 7.226 W | 3.505 W | +| **Total Energy** | 4193.45 J | 383.30 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +91.15% | 0.00e+00 | +95.218 | large | βœ… | +| `gpu_mj` | +83.82% | 0.00e+00 | +3.677 | large | βœ… | +| `ane_mj` | +100.00% | 0.00e+00 | +0.273 | small | βœ… | +| `dram_mj` | +87.81% | 0.00e+00 | +129.181 | large | βœ… | +| `time_s` | +81.05% | 0.00e+00 | +145.073 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 91.1% lower energy (Cohen’s d = +95.218, large) +- **`gpu_mj`**: 83.8% lower energy (Cohen’s d = +3.677, large) +- **`ane_mj`**: 100.0% lower energy (Cohen’s d = +0.273, small) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +129.181, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +145.073, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 1000 - raw_mac_report.md + +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 655.51 ms | 123.73 ms | +| **Average Power** | 7.179 W | 3.544 W | +| **Total Energy** | 4235.28 J | 394.69 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +91.17% | 0.00e+00 | +89.445 | large | βœ… | +| `gpu_mj` | +83.80% | 0.00e+00 | +3.683 | large | βœ… | +| `ane_mj` | +100.00% | 0.00e+00 | +0.272 | small | βœ… | +| `dram_mj` | +87.83% | 0.00e+00 | +117.690 | large | βœ… | +| `time_s` | +81.06% | 0.00e+00 | +134.006 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 91.2% lower energy (Cohen’s d = +89.445, large) +- **`gpu_mj`**: 83.8% lower energy (Cohen’s d = +3.683, large) +- **`ane_mj`**: 100.0% lower energy (Cohen’s d = +0.272, small) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +117.690, large) +- **`time_s`**: 81.1% lower time (Cohen’s d = +134.006, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +## How to read this, and what not to claim + +- A result is worth quoting only when Welch **and** Mann-Whitney agree and Cliff's Ξ΄ is at least small. Large sample sizes make tiny differences "significant" without making them relevant. +- Check the per-phase plots before trusting anything: a visible trend means the machine drifted during the campaign and the run should be repeated. +- These figures measure the client side only - no database engine, no network. They are a lower bound on the real saving. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/data/all_measurements.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/data/all_measurements.csv new file mode 100644 index 000000000..4ce71130f --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/data/all_measurements.csv @@ -0,0 +1,5401 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s,size,profiler,phase,variant +0,297.4059709817293,0.9431835074740536,0.0,64.00173800716793,0.0684758749994216,10,mac,phase-19,without +1,294.011382014694,0.9324180197543812,0.0,63.2712227690473,0.0676942920035799,10,mac,phase-19,without +2,296.82434397453113,0.9413389547272478,0.0,63.87657192792039,0.0683419589986442,10,mac,phase-19,without +3,295.5819955572358,0.937399011847616,0.0,63.609218661088235,0.0680559160027769,10,mac,phase-19,without 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/data/n10/with_smell.csv new file mode 100644 index 000000000..6f834725f --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/data/n10/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s,size,profiler,phase,variant +0,1431.6867028657243,0.8916668429916627,0.0,450.2257063150125,0.4793388750040321,10,mac,phase-19,with +1,1441.6843731260738,0.8978934783724855,0.0,453.3696959500776,0.4826861660039867,10,mac,phase-19,with +2,1441.9302878449869,0.8980466361832127,0.0,453.4470292994718,0.4827684999982011,10,mac,phase-19,with +3,1454.604502298105,0.9059402464026766,0.0,457.43271774843294,0.4870119170009275,10,mac,phase-19,with +4,1440.1645935339263,0.8969469465172544,0.0,452.8917674766566,0.4821773330040741,10,mac,phase-19,with +5,1443.7475013846054,0.8991784124696518,0.0,454.01849248884304,0.4833769159959047,10,mac,phase-19,with 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/data/n100/with_smell.csv new file mode 100644 index 000000000..dec9d18bc --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/data/n100/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s,size,profiler,phase,variant +0,1832.0288573050368,0.3348434297708107,0.0,485.7573635685151,0.520800792000955,100,mac,phase-19,with +1,1821.855037508852,0.3329839412038916,0.0,483.0598035044855,0.5179086249991087,100,mac,phase-19,with +2,1818.006657118434,0.3322805653351917,0.0,482.03941613176255,0.5168146250071004,100,mac,phase-19,with +3,1817.065228562979,0.3321084986315828,0.0,481.7897989648372,0.5165469999992638,100,mac,phase-19,with +4,1832.5450819984576,0.3349377811486223,0.0,485.8942391123064,0.5209475419978844,100,mac,phase-19,with +5,1811.56117568914,0.331102512325981,0.0,480.33041463130064,0.5149823329993524,100,mac,phase-19,with 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/data/n100/without_smell.csv new file mode 100644 index 000000000..cb616b5ee --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/data/n100/without_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s,size,profiler,phase,variant +0,246.0739988250765,0.0,0.0,60.74658181579221,0.0778715830019791,100,mac,phase-19,without +1,245.0826823466955,0.0,0.0,60.50186239055085,0.0775578749962733,100,mac,phase-19,without +2,244.5910373393436,0.0,0.0,60.38049339664647,0.0774022910045459,100,mac,phase-19,without +3,244.5508800746296,0.0,0.0,60.37058005115652,0.077389582998876,100,mac,phase-19,without +4,244.58959003848383,0.0,0.0,60.38013611151879,0.077401832997566,100,mac,phase-19,without +5,244.22606079392185,0.0,0.0,60.29039416762119,0.077286792002269,100,mac,phase-19,without 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differ diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n10/cleaned_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n10/cleaned_mac_report.md new file mode 100644 index 000000000..b599fc070 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n10/cleaned_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 880 samples (with smell) vs 882 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 481.32 ms | 73.56 ms | +| **Average Power** | 4.194 W | 5.170 W | +| **Total Energy** | 1776.26 J | 335.44 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.68% | 0.00e+00 | +6.469 | large | βœ… | +| `gpu_mj` | +86.23% | 6.17e-33 | +0.604 | medium | βœ… | +| `ane_mj` | +85.06% | 3.92e-22 | +0.473 | small | βœ… | +| `dram_mj` | +85.42% | 0.00e+00 | +28.839 | large | βœ… | +| `time_s` | +84.91% | 0.00e+00 | +53.990 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.7% lower energy (Cohen’s d = +6.469, large) +- **`gpu_mj`**: 86.2% lower energy (Cohen’s d = +0.604, medium) +- **`ane_mj`**: 85.1% lower energy (Cohen’s d = +0.473, small) +- **`dram_mj`**: 85.4% lower energy (Cohen’s d = +28.839, large) +- **`time_s`**: 84.9% lower time (Cohen’s d = +53.990, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n10/raw_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n10/raw_mac_report.md new file mode 100644 index 000000000..c56529e19 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n10/raw_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 482.89 ms | 73.43 ms | +| **Average Power** | 4.177 W | 5.169 W | +| **Total Energy** | 1815.47 J | 341.56 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.74% | 0.00e+00 | +6.500 | large | βœ… | +| `gpu_mj` | +86.51% | 1.12e-33 | +0.605 | medium | βœ… | +| `ane_mj` | +85.56% | 9.85e-23 | +0.475 | small | βœ… | +| `dram_mj` | +85.45% | 0.00e+00 | +28.600 | large | βœ… | +| `time_s` | +84.95% | 0.00e+00 | +52.041 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.7% lower energy (Cohen’s d = +6.500, large) +- **`gpu_mj`**: 86.5% lower energy (Cohen’s d = +0.605, medium) +- **`ane_mj`**: 85.6% lower energy (Cohen’s d = +0.475, small) +- **`dram_mj`**: 85.5% lower energy (Cohen’s d = +28.600, large) +- **`time_s`**: 84.9% lower time (Cohen’s d = +52.041, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n100/cleaned_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n100/cleaned_mac_report.md new file mode 100644 index 000000000..0f16c88e1 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n100/cleaned_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 891 samples (with smell) vs 875 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 521.44 ms | 78.82 ms | +| **Average Power** | 4.860 W | 4.414 W | +| **Total Energy** | 2258.08 J | 304.41 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.07% | 0.00e+00 | +6.228 | large | βœ… | +| `gpu_mj` | +80.99% | 2.11e-166 | +1.541 | large | βœ… | +| `ane_mj` | +86.38% | 1.05e-47 | +0.729 | medium | βœ… | +| `dram_mj` | +87.09% | 0.00e+00 | +20.546 | large | βœ… | +| `time_s` | +84.69% | 0.00e+00 | +75.359 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.1% lower energy (Cohen’s d = +6.228, large) +- **`gpu_mj`**: 81.0% lower energy (Cohen’s d = +1.541, large) +- **`ane_mj`**: 86.4% lower energy (Cohen’s d = +0.729, medium) +- **`dram_mj`**: 87.1% lower energy (Cohen’s d = +20.546, large) +- **`time_s`**: 84.7% lower time (Cohen’s d = +75.359, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n100/raw_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n100/raw_mac_report.md new file mode 100644 index 000000000..99df8502d --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n100/raw_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 520.34 ms | 78.33 ms | +| **Average Power** | 4.871 W | 4.426 W | +| **Total Energy** | 2281.01 J | 312.04 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.12% | 0.00e+00 | +6.230 | large | βœ… | +| `gpu_mj` | +81.23% | 4.98e-169 | +1.546 | large | βœ… | +| `ane_mj` | +86.55% | 2.91e-48 | +0.729 | medium | βœ… | +| `dram_mj` | +87.13% | 0.00e+00 | +20.514 | large | βœ… | +| `time_s` | +84.73% | 0.00e+00 | +74.836 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.1% lower energy (Cohen’s d = +6.230, large) +- **`gpu_mj`**: 81.2% lower energy (Cohen’s d = +1.546, large) +- **`ane_mj`**: 86.6% lower energy (Cohen’s d = +0.729, medium) +- **`dram_mj`**: 87.1% lower energy (Cohen’s d = +20.514, large) +- **`time_s`**: 84.7% lower time (Cohen’s d = +74.836, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n1000/cleaned_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n1000/cleaned_mac_report.md new file mode 100644 index 000000000..0ec7f1294 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n1000/cleaned_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 892 samples (with smell) vs 874 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 650.62 ms | 125.11 ms | +| **Average Power** | 7.226 W | 3.505 W | +| **Total Energy** | 4193.45 J | 383.30 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +91.15% | 0.00e+00 | +95.218 | large | βœ… | +| `gpu_mj` | +83.82% | 0.00e+00 | +3.677 | large | βœ… | +| `ane_mj` | +100.00% | 0.00e+00 | +0.273 | small | βœ… | +| `dram_mj` | +87.81% | 0.00e+00 | +129.181 | large | βœ… | +| `time_s` | +81.05% | 0.00e+00 | +145.073 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 91.1% lower energy (Cohen’s d = +95.218, large) +- **`gpu_mj`**: 83.8% lower energy (Cohen’s d = +3.677, large) +- **`ane_mj`**: 100.0% lower energy (Cohen’s d = +0.273, small) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +129.181, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +145.073, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n1000/raw_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n1000/raw_mac_report.md new file mode 100644 index 000000000..2c0845c14 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/consolidated-report/reports-source/n1000/raw_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 655.51 ms | 123.73 ms | +| **Average Power** | 7.179 W | 3.544 W | +| **Total Energy** | 4235.28 J | 394.69 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +91.17% | 0.00e+00 | +89.445 | large | βœ… | +| `gpu_mj` | +83.80% | 0.00e+00 | +3.683 | large | βœ… | +| `ane_mj` | +100.00% | 0.00e+00 | +0.272 | small | βœ… | +| `dram_mj` | +87.83% | 0.00e+00 | +117.690 | large | βœ… | +| `time_s` | +81.06% | 0.00e+00 | +134.006 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 91.2% lower energy (Cohen’s d = +89.445, large) +- **`gpu_mj`**: 83.8% lower energy (Cohen’s d = +3.683, large) +- **`ane_mj`**: 100.0% lower energy (Cohen’s d = +0.272, small) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +117.690, large) +- **`time_s`**: 81.1% lower time (Cohen’s d = +134.006, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/mac/phase-1/cleaned/csv/history_with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/mac/phase-1/cleaned/csv/history_with_smell.csv new file mode 100644 index 000000000..1a78eefcb --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/mac/phase-1/cleaned/csv/history_with_smell.csv @@ -0,0 +1,31 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1540.1575313933513,0.5065196880267544,0.0,465.7617371302016,0.49625229200319154 +1,1548.7716415444704,0.5093526556274295,0.0,468.36674527127565,0.49902783399738837 +2,1534.6656203497878,0.5047135344276873,0.0,464.10091869073943,0.4944827500003157 +3,1537.9614894800372,0.5057974642008454,0.0,465.09762824815067,0.49554470799921546 +4,1542.5662925003955,0.50731186992997,0.0,466.4901747962718,0.4970284160008305 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differ diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/mac/phase-9/raw/csv/history_with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/mac/phase-9/raw/csv/history_with_smell.csv new file mode 100644 index 000000000..31f3410fd --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/mac/phase-9/raw/csv/history_with_smell.csv @@ -0,0 +1,31 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1507.0006290040246,0.10121797087296991,0.0,462.93725944934005,0.49603141699481057 +1,1496.950668211312,0.10054296343155725,0.0,459.850000414799,0.4927234579954529 +2,1468.6940451139656,0.09864510221067248,0.0,451.169815810879,0.48342275000322843 +3,1468.9915282259046,0.09866508271789984,0.0,451.2611999907679,0.4835206669959007 +4,1479.1725047147079,0.09934888985232893,0.0,454.3887058879351,0.4868717499994091 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a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/results/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/results/cleaned/mac/mac_report.md new file mode 100644 index 000000000..b599fc070 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/results/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 880 samples (with smell) vs 882 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 481.32 ms | 73.56 ms | +| **Average Power** | 4.194 W | 5.170 W | +| **Total Energy** | 1776.26 J | 335.44 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.68% | 0.00e+00 | +6.469 | large | βœ… | +| `gpu_mj` | +86.23% | 6.17e-33 | +0.604 | medium | βœ… | +| `ane_mj` | +85.06% | 3.92e-22 | +0.473 | small | βœ… | +| `dram_mj` | +85.42% | 0.00e+00 | +28.839 | large | βœ… | +| `time_s` | +84.91% | 0.00e+00 | +53.990 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.7% lower energy (Cohen’s d = +6.469, large) +- **`gpu_mj`**: 86.2% lower energy (Cohen’s d = +0.604, medium) +- **`ane_mj`**: 85.1% lower energy (Cohen’s d = +0.473, small) +- **`dram_mj`**: 85.4% lower energy (Cohen’s d = +28.839, large) +- **`time_s`**: 84.9% lower time (Cohen’s d = +53.990, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/results/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/results/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..fe155cb4b --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/results/cleaned/mac/with_smell.csv @@ -0,0 +1,881 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1507.0006290040246,0.1012179708729699,0.0,462.9372594493401,0.4960314169948105 +1,1496.950668211312,0.1005429634315572,0.0,459.850000414799,0.4927234579954529 +2,1468.6940451139656,0.0986451022106724,0.0,451.169815810879,0.4834227500032284 +3,1468.9915282259046,0.0986650827178998,0.0,451.2611999907679,0.4835206669959007 +4,1479.172504714708,0.0993488898523289,0.0,454.3887058879351,0.4868717499994091 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**hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 482.89 ms | 73.43 ms | +| **Average Power** | 4.177 W | 5.169 W | +| **Total Energy** | 1815.47 J | 341.56 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.74% | 0.00e+00 | +6.500 | large | βœ… | +| `gpu_mj` | +86.51% | 1.12e-33 | +0.605 | medium | βœ… | +| `ane_mj` | +85.56% | 9.85e-23 | +0.475 | small | βœ… | +| `dram_mj` | +85.45% | 0.00e+00 | +28.600 | large | βœ… | +| `time_s` | +84.95% | 0.00e+00 | +52.041 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.7% lower energy (Cohen’s d = +6.500, large) +- **`gpu_mj`**: 86.5% lower energy (Cohen’s d = +0.605, medium) +- **`ane_mj`**: 85.6% lower energy (Cohen’s d = +0.475, small) +- **`dram_mj`**: 85.5% lower energy (Cohen’s d = +28.600, large) +- **`time_s`**: 84.9% lower time (Cohen’s d = +52.041, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/results/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/results/raw/mac/with_smell.csv new file mode 100644 index 000000000..29d534adc --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n10/results/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1507.0006290040246,0.1012179708729699,0.0,462.9372594493401,0.4960314169948105 +1,1496.950668211312,0.1005429634315572,0.0,459.850000414799,0.4927234579954529 +2,1468.6940451139656,0.0986451022106724,0.0,451.169815810879,0.4834227500032284 +3,1468.9915282259046,0.0986650827178998,0.0,451.2611999907679,0.4835206669959007 +4,1479.172504714708,0.0993488898523289,0.0,454.3887058879351,0.4868717499994091 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/mac/phase-9/raw/csv/history_without_smell.csv new file mode 100644 index 000000000..9713f2467 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/mac/phase-9/raw/csv/history_without_smell.csv @@ -0,0 +1,31 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,259.2976060090398,0.9378436735173551,0.0,59.34286692773609,0.0769311670010211 +1,255.2444208838569,0.9231838620144487,0.0,58.41525471712115,0.07572862500092015 +2,271.6565778521991,0.9825443698819872,0.0,62.17134202529126,0.08059795799636049 +3,281.010318201593,1.0163755584742078,0.0,64.31203964828177,0.0833731250022538 +4,276.562361563488,1.0002879128637008,0.0,63.294080003616926,0.08205345799797215 +5,274.2101672039858,0.9917803503262146,0.0,62.75575664995185,0.0813555839995388 +6,274.5216566791403,0.9929069647911035,0.0,62.827044151437065,0.08144800000445684 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Binary files /dev/null and b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/mac/phase-9/raw/plots/violins/time_violin.png differ diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/cleaned/mac/mac_report.md new file mode 100644 index 000000000..0f16c88e1 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 891 samples (with smell) vs 875 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 521.44 ms | 78.82 ms | +| **Average Power** | 4.860 W | 4.414 W | +| **Total Energy** | 2258.08 J | 304.41 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.07% | 0.00e+00 | +6.228 | large | βœ… | +| `gpu_mj` | +80.99% | 2.11e-166 | +1.541 | large | βœ… | +| `ane_mj` | +86.38% | 1.05e-47 | +0.729 | medium | βœ… | +| `dram_mj` | +87.09% | 0.00e+00 | +20.546 | large | βœ… | +| `time_s` | +84.69% | 0.00e+00 | +75.359 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.1% lower energy (Cohen’s d = +6.228, large) +- **`gpu_mj`**: 81.0% lower energy (Cohen’s d = +1.541, large) +- **`ane_mj`**: 86.4% lower energy (Cohen’s d = +0.729, medium) +- **`dram_mj`**: 87.1% lower energy (Cohen’s d = +20.546, large) +- **`time_s`**: 84.7% lower time (Cohen’s d = +75.359, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..6bcc28fee --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/cleaned/mac/with_smell.csv @@ -0,0 +1,892 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1754.9343348502491,0.9659333379641896,0.0,478.1370022922739,0.5167249579972122 +1,1749.9576772420037,0.9631941360465022,0.0,476.7810973430186,0.5152596249972703 +2,1765.8187969550952,0.9719242543216248,0.0,481.1025058892042,0.5199297919971286 +3,1744.1414360162792,0.9599928189430628,0.0,475.1964453768161,0.5135470839959453 +4,1751.8730274703044,0.9642483638900476,0.0,477.3029401255735,0.5158235830022022 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/raw/mac/mac_report.md new file mode 100644 index 000000000..99df8502d --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 520.34 ms | 78.33 ms | +| **Average Power** | 4.871 W | 4.426 W | +| **Total Energy** | 2281.01 J | 312.04 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.12% | 0.00e+00 | +6.230 | large | βœ… | +| `gpu_mj` | +81.23% | 4.98e-169 | +1.546 | large | βœ… | +| `ane_mj` | +86.55% | 2.91e-48 | +0.729 | medium | βœ… | +| `dram_mj` | +87.13% | 0.00e+00 | +20.514 | large | βœ… | +| `time_s` | +84.73% | 0.00e+00 | +74.836 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.1% lower energy (Cohen’s d = +6.230, large) +- **`gpu_mj`**: 81.2% lower energy (Cohen’s d = +1.546, large) +- **`ane_mj`**: 86.6% lower energy (Cohen’s d = +0.729, medium) +- **`dram_mj`**: 87.1% lower energy (Cohen’s d = +20.514, large) +- **`time_s`**: 84.7% lower time (Cohen’s d = +74.836, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/raw/mac/with_smell.csv new file mode 100644 index 000000000..15b55e866 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n100/results/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1754.9343348502491,0.9659333379641896,0.0,478.1370022922739,0.5167249579972122 +1,1749.9576772420037,0.9631941360465022,0.0,476.7810973430186,0.5152596249972703 +2,1765.8187969550952,0.9719242543216248,0.0,481.1025058892042,0.5199297919971286 +3,1744.1414360162792,0.9599928189430628,0.0,475.1964453768161,0.5135470839959453 +4,1751.8730274703044,0.9642483638900476,0.0,477.3029401255735,0.5158235830022022 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100644 index 000000000..4bb1da5a1 Binary files /dev/null and b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/mac/phase-9/raw/plots/violins/time_violin.png differ diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/cleaned/mac/mac_report.md new file mode 100644 index 000000000..0ec7f1294 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 892 samples (with smell) vs 874 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 650.62 ms | 125.11 ms | +| **Average Power** | 7.226 W | 3.505 W | +| **Total Energy** | 4193.45 J | 383.30 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +91.15% | 0.00e+00 | +95.218 | large | βœ… | +| `gpu_mj` | +83.82% | 0.00e+00 | +3.677 | large | βœ… | +| `ane_mj` | +100.00% | 0.00e+00 | +0.273 | small | βœ… | +| `dram_mj` | +87.81% | 0.00e+00 | +129.181 | large | βœ… | +| `time_s` | +81.05% | 0.00e+00 | +145.073 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 91.1% lower energy (Cohen’s d = +95.218, large) +- **`gpu_mj`**: 83.8% lower energy (Cohen’s d = +3.677, large) +- **`ane_mj`**: 100.0% lower energy (Cohen’s d = +0.273, small) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +129.181, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +145.073, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..47d4d1d1a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/cleaned/mac/with_smell.csv @@ -0,0 +1,893 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,4298.324586146077,2.34866663806101,0.0,675.5559166546752,0.666461957996944 +1,4386.990791615231,2.397115133407328,0.0,689.4913416118936,0.6802097920008237 +2,4314.322225328852,2.3574079791773657,0.0,678.0702218416924,0.6689424170035636 +3,4320.785126850016,2.3609394019174617,0.0,679.0859783937804,0.6699445000049309 +4,4316.092062346529,2.358375043687017,0.0,678.3483822841725,0.6692168330046115 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+28,374.88398411686927,1.0170654931530154,0.0,78.56830934607044,0.1197465830045985 diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/raw/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/raw/mac/mac_report.md new file mode 100644 index 000000000..2c0845c14 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 655.51 ms | 123.73 ms | +| **Average Power** | 7.179 W | 3.544 W | +| **Total Energy** | 4235.28 J | 394.69 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +91.17% | 0.00e+00 | +89.445 | large | βœ… | +| `gpu_mj` | +83.80% | 0.00e+00 | +3.683 | large | βœ… | +| `ane_mj` | +100.00% | 0.00e+00 | +0.272 | small | βœ… | +| `dram_mj` | +87.83% | 0.00e+00 | +117.690 | large | βœ… | +| `time_s` | +81.06% | 0.00e+00 | +134.006 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 91.2% lower energy (Cohen’s d = +89.445, large) +- **`gpu_mj`**: 83.8% lower energy (Cohen’s d = +3.683, large) +- **`ane_mj`**: 100.0% lower energy (Cohen’s d = +0.272, small) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +117.690, large) +- **`time_s`**: 81.1% lower time (Cohen’s d = +134.006, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/raw/mac/with_smell.csv new file mode 100644 index 000000000..5567cf072 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/out-tmp/fetchtype-n1000/results/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,4298.324586146077,2.34866663806101,0.0,675.5559166546752,0.666461957996944 +1,4386.990791615231,2.397115133407328,0.0,689.4913416118936,0.6802097920008237 +2,4314.322225328852,2.3574079791773657,0.0,678.0702218416924,0.6689424170035636 +3,4320.785126850016,2.3609394019174617,0.0,679.0859783937804,0.6699445000049309 +4,4316.092062346529,2.358375043687017,0.0,678.3483822841725,0.6692168330046115 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000000000..b599fc070 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n10/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 880 samples (with smell) vs 882 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 481.32 ms | 73.56 ms | +| **Average Power** | 4.194 W | 5.170 W | +| **Total Energy** | 1776.26 J | 335.44 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.68% | 0.00e+00 | +6.469 | large | βœ… | +| `gpu_mj` | +86.23% | 6.17e-33 | +0.604 | medium | βœ… | +| `ane_mj` | +85.06% | 3.92e-22 | +0.473 | small | βœ… | +| `dram_mj` | +85.42% | 0.00e+00 | +28.839 | large | βœ… | +| `time_s` | +84.91% | 0.00e+00 | +53.990 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.7% lower energy (Cohen’s d = +6.469, large) +- **`gpu_mj`**: 86.2% lower energy (Cohen’s d = +0.604, medium) +- **`ane_mj`**: 85.1% lower energy (Cohen’s d = +0.473, small) +- **`dram_mj`**: 85.4% lower energy (Cohen’s d = +28.839, large) +- **`time_s`**: 84.9% lower time (Cohen’s d = +53.990, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n10/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n10/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..fe155cb4b --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n10/cleaned/mac/with_smell.csv @@ -0,0 +1,881 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1507.0006290040246,0.1012179708729699,0.0,462.9372594493401,0.4960314169948105 +1,1496.950668211312,0.1005429634315572,0.0,459.850000414799,0.4927234579954529 +2,1468.6940451139656,0.0986451022106724,0.0,451.169815810879,0.4834227500032284 +3,1468.9915282259046,0.0986650827178998,0.0,451.2611999907679,0.4835206669959007 +4,1479.172504714708,0.0993488898523289,0.0,454.3887058879351,0.4868717499994091 +5,1500.238416336118,0.1007637856313158,0.0,460.85996754909496,0.4938056250030058 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`MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 482.89 ms | 73.43 ms | +| **Average Power** | 4.177 W | 5.169 W | +| **Total Energy** | 1815.47 J | 341.56 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.74% | 0.00e+00 | +6.500 | large | βœ… | +| `gpu_mj` | +86.51% | 1.12e-33 | +0.605 | medium | βœ… | +| `ane_mj` | +85.56% | 9.85e-23 | +0.475 | small | βœ… | +| `dram_mj` | +85.45% | 0.00e+00 | +28.600 | large | βœ… | +| `time_s` | +84.95% | 0.00e+00 | +52.041 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.7% lower energy (Cohen’s d = +6.500, large) +- **`gpu_mj`**: 86.5% lower energy (Cohen’s d = +0.605, medium) +- **`ane_mj`**: 85.6% lower energy (Cohen’s d = +0.475, small) +- **`dram_mj`**: 85.5% lower energy (Cohen’s d = +28.600, large) +- **`time_s`**: 84.9% lower time (Cohen’s d = +52.041, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n10/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n10/raw/mac/with_smell.csv new file mode 100644 index 000000000..29d534adc --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n10/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1507.0006290040246,0.1012179708729699,0.0,462.9372594493401,0.4960314169948105 +1,1496.950668211312,0.1005429634315572,0.0,459.850000414799,0.4927234579954529 +2,1468.6940451139656,0.0986451022106724,0.0,451.169815810879,0.4834227500032284 +3,1468.9915282259046,0.0986650827178998,0.0,451.2611999907679,0.4835206669959007 +4,1479.172504714708,0.0993488898523289,0.0,454.3887058879351,0.4868717499994091 +5,1500.238416336118,0.1007637856313158,0.0,460.85996754909496,0.4938056250030058 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**chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 521.44 ms | 78.82 ms | +| **Average Power** | 4.860 W | 4.414 W | +| **Total Energy** | 2258.08 J | 304.41 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.07% | 0.00e+00 | +6.228 | large | βœ… | +| `gpu_mj` | +80.99% | 2.11e-166 | +1.541 | large | βœ… | +| `ane_mj` | +86.38% | 1.05e-47 | +0.729 | medium | βœ… | +| `dram_mj` | +87.09% | 0.00e+00 | +20.546 | large | βœ… | +| `time_s` | +84.69% | 0.00e+00 | +75.359 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.1% lower energy (Cohen’s d = +6.228, large) +- **`gpu_mj`**: 81.0% lower energy (Cohen’s d = +1.541, large) +- **`ane_mj`**: 86.4% lower energy (Cohen’s d = +0.729, medium) +- **`dram_mj`**: 87.1% lower energy (Cohen’s d = +20.546, large) +- **`time_s`**: 84.7% lower time (Cohen’s d = +75.359, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n100/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n100/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..6bcc28fee --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n100/cleaned/mac/with_smell.csv @@ -0,0 +1,892 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1754.9343348502491,0.9659333379641896,0.0,478.1370022922739,0.5167249579972122 +1,1749.9576772420037,0.9631941360465022,0.0,476.7810973430186,0.5152596249972703 +2,1765.8187969550952,0.9719242543216248,0.0,481.1025058892042,0.5199297919971286 +3,1744.1414360162792,0.9599928189430628,0.0,475.1964453768161,0.5135470839959453 +4,1751.8730274703044,0.9642483638900476,0.0,477.3029401255735,0.5158235830022022 +5,1770.1285132468035,0.9742963650956062,0.0,482.2767007223251,0.5211987500006217 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n100/raw/mac/mac_report.md new file mode 100644 index 000000000..99df8502d --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n100/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 520.34 ms | 78.33 ms | +| **Average Power** | 4.871 W | 4.426 W | +| **Total Energy** | 2281.01 J | 312.04 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.12% | 0.00e+00 | +6.230 | large | βœ… | +| `gpu_mj` | +81.23% | 4.98e-169 | +1.546 | large | βœ… | +| `ane_mj` | +86.55% | 2.91e-48 | +0.729 | medium | βœ… | +| `dram_mj` | +87.13% | 0.00e+00 | +20.514 | large | βœ… | +| `time_s` | +84.73% | 0.00e+00 | +74.836 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.1% lower energy (Cohen’s d = +6.230, large) +- **`gpu_mj`**: 81.2% lower energy (Cohen’s d = +1.546, large) +- **`ane_mj`**: 86.6% lower energy (Cohen’s d = +0.729, medium) +- **`dram_mj`**: 87.1% lower energy (Cohen’s d = +20.514, large) +- **`time_s`**: 84.7% lower time (Cohen’s d = +74.836, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. 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a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n1000/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n1000/cleaned/mac/mac_report.md new file mode 100644 index 000000000..0ec7f1294 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n1000/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 892 samples (with smell) vs 874 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 650.62 ms | 125.11 ms | +| **Average Power** | 7.226 W | 3.505 W | +| **Total Energy** | 4193.45 J | 383.30 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +91.15% | 0.00e+00 | +95.218 | large | βœ… | +| `gpu_mj` | +83.82% | 0.00e+00 | +3.677 | large | βœ… | +| `ane_mj` | +100.00% | 0.00e+00 | +0.273 | small | βœ… | +| `dram_mj` | +87.81% | 0.00e+00 | +129.181 | large | βœ… | +| `time_s` | +81.05% | 0.00e+00 | +145.073 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 91.1% lower energy (Cohen’s d = +95.218, large) +- **`gpu_mj`**: 83.8% lower energy (Cohen’s d = +3.677, large) +- **`ane_mj`**: 100.0% lower energy (Cohen’s d = +0.273, small) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +129.181, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +145.073, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n1000/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n1000/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..47d4d1d1a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n1000/cleaned/mac/with_smell.csv @@ -0,0 +1,893 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,4298.324586146077,2.34866663806101,0.0,675.5559166546752,0.666461957996944 +1,4386.990791615231,2.397115133407328,0.0,689.4913416118936,0.6802097920008237 +2,4314.322225328852,2.3574079791773657,0.0,678.0702218416924,0.6689424170035636 +3,4320.785126850016,2.3609394019174617,0.0,679.0859783937804,0.6699445000049309 +4,4316.092062346529,2.358375043687017,0.0,678.3483822841725,0.6692168330046115 +5,4324.958738322495,2.363219923508882,0.0,679.7419334915267,0.6705916249993606 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`mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 655.51 ms | 123.73 ms | +| **Average Power** | 7.179 W | 3.544 W | +| **Total Energy** | 4235.28 J | 394.69 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +91.17% | 0.00e+00 | +89.445 | large | βœ… | +| `gpu_mj` | +83.80% | 0.00e+00 | +3.683 | large | βœ… | +| `ane_mj` | +100.00% | 0.00e+00 | +0.272 | small | βœ… | +| `dram_mj` | +87.83% | 0.00e+00 | +117.690 | large | βœ… | +| `time_s` | +81.06% | 0.00e+00 | +134.006 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 91.2% lower energy (Cohen’s d = +89.445, large) +- **`gpu_mj`**: 83.8% lower energy (Cohen’s d = +3.683, large) +- **`ane_mj`**: 100.0% lower energy (Cohen’s d = +0.272, small) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +117.690, large) +- **`time_s`**: 81.1% lower time (Cohen’s d = +134.006, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n1000/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n1000/raw/mac/with_smell.csv new file mode 100644 index 000000000..5567cf072 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign1/results/n1000/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,4298.324586146077,2.34866663806101,0.0,675.5559166546752,0.666461957996944 +1,4386.990791615231,2.397115133407328,0.0,689.4913416118936,0.6802097920008237 +2,4314.322225328852,2.3574079791773657,0.0,678.0702218416924,0.6689424170035636 +3,4320.785126850016,2.3609394019174617,0.0,679.0859783937804,0.6699445000049309 +4,4316.092062346529,2.358375043687017,0.0,678.3483822841725,0.6692168330046115 +5,4324.958738322495,2.363219923508882,0.0,679.7419334915267,0.6705916249993606 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Internal working document. Companion to +`campaign1/campaign1-interpretation.md`, whose conclusions this campaign +was designed to refine, not replace.* + +*Context note: SonarQube already ships a built-in rule under the identifier S6904, so the +four historical PRs this harness was built to support (#122, #125, #155, #325) are being +closed as duplicates rather than merged. This document is kept regardless, as independent +evidence that the underlying code smell has a real, measurable cost β€” useful to justify why +the built-in rule matters and to inform its configuration (severity, default scope), even +though no new rule will be shipped from this work.* + +--- + +## Summary + +**Campaign 2 confirms campaign 1's headline finding and adds one genuine complication.** +Six collection sizes were measured this time β€” 10, 30, 50, 100, 500 and 1000 β€” instead of +three, specifically to locate the relevance threshold that campaign 1 left open. At every +size, the effect is large, robust, and points the same way across all 30 phases +(sign test p β‰ˆ 1.9 Γ— 10⁻⁹ at each of the six sizes independently). + +**What is confirmed.** At the three sizes shared with campaign 1 (10, 100, 1000), the +relative gap reproduces within 0.6 percentage points despite the two campaigns running on +different days: 81.0% β†’ 81.30% at N=10, 86.3% β†’ 86.67% at N=100, 90.7% β†’ 90.13% at N=1000. +Absolute joule levels drift more than that between sessions (up to Β±15% at N=100), which +is exactly the reason campaign 1 argued for reporting relative, paired figures rather than +absolute totals β€” campaign 2 is a direct demonstration of that argument. + +**What is new, and not entirely clean.** The extra sizes do not simply fill in a smooth +curve between the three original points. Energy per hydrated row goes 0.33 β†’ 0.36 β†’ 0.47 +Β΅J from N=10 to N=30 to N=50, then **drops to 0.38 Β΅J at N=100** before resuming its climb +to 0.66 and 0.94 Β΅J at N=500 and N=1000. Fitted on energy *per read*, a single exponent +across all six points looks excellent (N^1.22, RΒ² = 0.995) simply because per-read values +span two orders of magnitude on the back of N itself β€” that fit hides the kink. Fitted on +energy *per row* instead, which divides out that trivial NΒΉ factor, the same six points +give RΒ² = 0.85: the local step from N=50 to N=100 is actually sub-linear (exponent β‰ˆ ++0.68), the opposite of what an averaged curve suggests. This is discussed in Β§ 4 and Β§ 6 +rather than smoothed over. + +**Also new**: the relative percentage gap is already 81% at N=10 and 84% at N=30 β€” large +from the smallest size tested. What grows with collection size is not "whether the effect +exists" but its **absolute magnitude** and its **share of average power** (EAGER overtakes +LAZY in average power draw somewhere between N=50 and N=100, a crossover campaign 1 could +only bracket, not locate). + +**Conclusion, updated from campaign 1**: there is no single clean scaling law. The coarse +trend holds (energy per row roughly triples from N=10 to N=1000, energy per read grows by +two orders of magnitude), but the fine-grained data shows a local anomaly around N=50–100 +whose cause is not established. + +--- + +## 1. What was measured + +Same scenario, same machine, same protocol as campaign 1 β€” see that document's Β§ 1 for the +full description. The only change is scope: **six** collection sizes instead of three, each +still measured over 30 phases Γ— 30 iterations = 900 runs per variant, with the same +60-second cooldown and randomised variant order. + +| | Campaign 1 | Campaign 2 | +|---|---|---| +| Date | 2026-08-04 | 2026-08-05 | +| Sizes | 10, 100, 1000 | 10, 30, 50, 100, 500, 1000 | +| Runs per variant per size | 900 | 900 | +| Rows hydrated per run (target) | β‰ˆ 5,000,000 | β‰ˆ 5,000,000 (same `CONFIGS`, unchanged) | +| Level 1 (JPA) | single run | **not re-run**; campaign 2 covers level 2 only | + +Level 1 figures quoted below are therefore still the single campaign-1 run +(`level1-jpa/results/level1-20260802-120143.txt`); nothing in this document changes them. +`ane_mj` is again **excluded from every total**, for the same reason as campaign 1 (see Β§ 5 +here and Β§ 4 of the campaign 1 document). + +--- + +## 2. Results + +### Level 2 β€” energy cost, all six sizes + +Means over 900 runs per variant, outliers removed at 3Οƒ, totals = CPU + GPU + DRAM: + +| N | EAGER | LAZY | Gap | Relative gap | Ratio | +|---:|---:|---:|---:|---:|---:| +| 10 | 2,049.0 mJ | 383.1 mJ | 1,665.9 mJ | +81.30% | x5.35 | +| 30 | 2,150.3 mJ | 345.6 mJ | 1,804.7 mJ | +83.93% | x6.22 | +| 50 | 2,743.9 mJ | 389.8 mJ | 2,354.0 mJ | +85.79% | x7.04 | +| 100 | 2,175.2 mJ | 290.0 mJ | 1,885.2 mJ | +86.67% | x7.50 | +| 500 | 3,787.8 mJ | 488.5 mJ | 3,299.3 mJ | +87.10% | x7.75 | +| 1000 | 5,192.2 mJ | 512.7 mJ | 4,679.5 mJ | +90.13% | x10.13 | + +**The relative gap climbs smoothly and monotonically** from 81.3% to 90.1% across the six +sizes β€” this part of the curve is clean. **The absolute EAGER mean does not climb +smoothly**: 2,743.9 mJ at N=50 is higher than 2,175.2 mJ at N=100, a reversal that recurs at +the phase level (Β§ 4) and is not an artefact of a single bad phase. + +### The correct statistic: at the phase level + +As in campaign 1, the 900 runs within a phase are not independent (they share thermal and +system state), so the valid unit of analysis is the phase, n = 30 pairs: + +| N | Gap per phase | 95% CI | Relative gap | Phases same direction | r(EAGER, LAZY) | +|---:|---:|---:|---:|:---:|---:| +| 10 | 1,665.9 mJ | [1,557.4, 1,774.5] | 81.30% | **30 / 30** | +0.885 | +| 30 | 1,831.3 mJ | [1,709.4, 1,953.3] | 84.13% | **30 / 30** | +0.966 | +| 50 | 2,354.0 mJ | [2,223.4, 2,484.6] | 85.79% | **30 / 30** | +0.969 | +| 100 | 1,877.1 mJ | [1,856.6, 1,897.7] | 86.25% | **30 / 30** | **+0.101** | +| 500 | 3,299.3 mJ | [3,141.4, 3,457.2] | 87.10% | **30 / 30** | +0.981 | +| 1000 | 4,675.7 mJ | [4,525.2, 4,826.2] | 90.02% | **30 / 30** | +0.732 | + +All 30 phases point the same way at every one of the six sizes. **Sign test at each size: +p = 2 Γ— 0.5³⁰ β‰ˆ 1.9 Γ— 10⁻⁹.** Six independent confirmations of the same result, at six +different sizes, each with the maximum significance a 30-phase design can produce. + +**One entry does not fit the pattern**: at N=100, the correlation between a phase's EAGER +mean and its LAZY mean is +0.101, an order of magnitude weaker than at every other size +(+0.73 to +0.97). At every other size, whatever perturbs a phase (thermal state, system +load) perturbs both variants together, which is what produces the strong positive +correlation and is why the relative gap stays stable despite that perturbation (see Β§ 4). +At N=100 this coupling is essentially absent β€” investigated in Β§ 4. + +Distributions remain **strictly disjoint** on `cpu_mj`, `dram_mj` and `time_s` at all six +sizes (checked directly on the pooled 900-sample raw values, not just phase means): the +highest LAZY value is always below the lowest EAGER value. + +Breakdown of the gap by component: + +| N | CPU | DRAM | GPU | +|---:|---:|---:|---:| +| 10 | 76.4% | 23.5% | 0.1% | +| 30 | 77.3% | 22.6% | 0.1% | +| 50 | 81.5% | 18.4% | 0.1% | +| 100 | 78.0% | 21.9% | 0.1% | +| 500 | 84.2% | 15.7% | 0.1% | +| 1000 | 87.1% | 12.9% | 0.1% | + +Same reading as campaign 1: CPU dominates and its share grows with size, GPU is +negligible. The N=100 row is again slightly out of trend (78.0% sits below both its +neighbours, 81.5% at N=50 and 84.2% at N=500) β€” consistent with the same anomaly. + +--- + +## 3. Consistency with campaign 1 + +The three sizes tested in both campaigns give a direct check on reproducibility, run on two +different days: + +| N | Relative gap, campaign 1 | Relative gap, campaign 2 | Ξ” | EAGER mean drift | LAZY mean drift | +|---:|---:|---:|---:|---:|---:| +| 10 | 81.0% | 81.30% | +0.3 pt | +0.1% | βˆ’1.4% | +| 100 | 86.3% | 86.67% | +0.4 pt | βˆ’13.4% | βˆ’15.6% | +| 1000 | 90.7% | 90.13% | βˆ’0.6 pt | +3.0% | +9.6% | + +**The relative gap is remarkably stable** β€” never moving by more than 0.6 percentage +points between two independent measurement sessions. **The absolute joule levels are not**: +at N=100 both variants' absolute means shifted by 13-16% between campaigns (same machine, +same code, different day β€” almost certainly ambient temperature or background system load). +This is precisely why campaign 1 warned against quoting absolute joule figures as if they +were portable constants, and why every headline number in both documents is a relative +comparison or a per-row normalisation, never a bare joule total. + +Energy per hydrated row at the shared sizes: 0.33 β†’ 0.33 Β΅J (N=10), 0.43 β†’ 0.38 Β΅J (N=100), +0.92 β†’ 0.94 Β΅J (N=1000). N=10 and N=1000 reproduce closely; N=100 is the one point where +the two campaigns disagree by more than rounding β€” consistent with it being the same +anomalous size in both the correlation breakdown above and the scaling analysis below. + +--- + +## 4. The number to publish, and the complication + +### Energy per hydrated row and per parent read + +| N | Β΅J / hydrated row | Range (incl. / excl. high-regime phases) | Β΅J / parent read | +|---:|---:|---:|---:| +| 10 | 0.33 | 0.30 – 0.33 | 3.3 | +| 30 | 0.36 | 0.34 – 0.36 | 10.8 | +| 50 | 0.47 | 0.47 (no high-regime phases at this size) | 23.5 | +| 100 | 0.38 | 0.38 (no meaningful spread) | 37.7 | +| 500 | 0.66 | 0.61 – 0.66 | 329.9 | +| 1000 | 0.94 | 0.90 – 0.94 | 935.9 | + +**Energy per row is not monotonic in N.** It rises from N=10 through N=50, dips at N=100, +then resumes rising through N=500 and N=1000. This is not a rounding effect nor the product +of a single outlier phase: excluding every phase flagged as high-regime at N=100 changes +the figure by under 1%, and the EAGER-only mean (not just the EAGER-minus-LAZY gap) is +itself lower at N=100 (2,175.2 mJ) than at N=50 (2,743.9 mJ). Whatever is happening at +N=100 affects the EAGER variant's own absolute cost, not only its difference from LAZY. + +### Scaling exponent β€” coarse view versus fine view + +Campaign 1, with only three points, fitted **N^1.10–1.12** between 10 and 100 and +**N^1.32–1.37** between 100 and 1000, and reported an overall picture of smooth +super-linear growth. Campaign 2's coarse view (using only the shared points, 10 β†’ 100 β†’ +1000) is close: **N^1.05** and **N^1.40** β€” a reasonable, if not exact, reproduction, well +within what a single-day measurement session can be expected to drift. + +The fine view, using all six points, breaks the smooth picture: + +| Step | Energy per read | Exponent | +|---|---:|---:| +| N=10 β†’ 30 | x3.25 | +1.07 | +| N=30 β†’ 50 | x2.17 | +1.52 | +| **N=50 β†’ 100** | **x1.60** | **+0.68** | +| N=100 β†’ 500 | x8.75 | +1.35 | +| N=500 β†’ 1000 | x2.84 | +1.50 | + +Four of the five steps are super-linear, consistent with campaign 1's reading. **One step, +N=50 β†’ 100, is sub-linear** (exponent below 1, meaning energy per read grows slower than +collection size there β€” the opposite of the rule's expected direction, though the smell is +still far more expensive than LAZY in absolute terms at both sizes). A single exponent +fitted across all six points on **energy per read** gives **N^1.22** with RΒ² = 0.995 β€” an +excellent-looking fit that is, on its own, misleading: energy per read spans almost three +orders of magnitude (3.3 Β΅J to 936 Β΅J) driven mostly by the trivial fact that N itself +grows by a factor of 100, so a log-log fit on that quantity looks clean even with the kink +present, because the kink is a small wobble relative to the dominant NΒΉ trend. + +Fitting the **same six points on energy per hydrated row** instead β€” which divides out +that trivial NΒΉ factor and isolates exactly the part of the cost that is not simple +proportionality β€” gives exponent β‰ˆ **N^0.22** (i.e. total exponent on energy-per-read of +1 + 0.22 β‰ˆ 1.22, the same headline number) but with **RΒ² = 0.85**, visibly below 1. That +weaker fit is the honest signal that no single power law actually describes the residual, +size-dependent part of the cost: the curve has a genuine kink around N=50–100. Quoting only +the RΒ² = 0.995 energy-per-read fit would hide exactly the anomaly this campaign was +designed to surface; both numbers belong together wherever the exponent is cited. + +**What this does not do**: it does not overturn the case for the rule. Even at its lowest +point (N=100), energy per row (0.38 Β΅J) is still higher than at N=10 (0.33 Β΅J) and the +relative gap keeps climbing smoothly through the same range (86.67% at N=100, up from +85.79% at N=50). The kink is in the second-order shape of the curve, not in the sign or the +overall order of magnitude of the effect. + +--- + +## 5. Quality control + +### The N=100 anomaly + +Three independent signals converge on N=100 as an outlier among the six sizes: + +1. Phase-level correlation between EAGER and LAZY collapses to +0.101, versus +0.73 to + +0.97 everywhere else. +2. EAGER's own per-phase `total_mj` mean is unusually flat at N=100 (coefficient of + variation 1.25%, versus 9–17% at the other five sizes) β€” every other size shows + noticeably more phase-to-phase spread on the EAGER side. +3. Energy per row dips at N=100 rather than continuing to climb from N=50. + +A plausible reading is that whatever produces the shared, correlated phase-to-phase +variation at other sizes (ambient temperature, background load, core scheduling) happened, +by chance, to leave the EAGER runs at N=100 largely undisturbed while still perturbing +LAZY β€” which would simultaneously explain the low correlation, the flat EAGER phases, and +the depressed gap. **This is a hypothesis, not a finding**: nothing in the collected data +identifies a mechanism, and it was not reproduced by re-running N=100 within this campaign. +It should be treated as an open question, not folded into the headline scaling narrative. + +### Bimodality, the other five sizes + +As in campaign 1, several sizes show a subset of phases sitting well above the rest, and +in every one of those cases **both variants are affected together**: + +| N | High-regime phases (of 30) | Affects | +|---:|---|---| +| 10 | 5, 7-11, 13, 16 | EAGER and LAZY together | +| 30 | 26-30 | EAGER and LAZY together | +| 50 | none detected | β€” | +| 500 | 3-10 | EAGER and LAZY together | +| 1000 | 6-11, 15, 24 | EAGER and LAZY together | + +This is the same paired-cancellation pattern documented in campaign 1 Β§ 4: because both +variants are measured within the same phase in randomised order, a shared perturbation +moves both means together and the *relative* gap barely shifts (see the stability of the +percentages in Β§ 3). N=100 is the one size where this cancellation mechanism does not +appear to operate, which is consistent with β€” and adds weight to β€” treating it as the +outlier. + +### `ane_mj`, again excluded + +Excluded from every total in this document, as in campaign 1. At N=100 specifically, +`ane_mj` was measured as exactly zero for every one of the 1,800 samples (both variants) β€” +not just small, but identically zero, which is itself consistent with `ane_mj` being an +unreliable counter on this hardware rather than a real physical measurement (see campaign 1 +Β§ 4 for the same conclusion at other sizes, where `ane_mj` varied by two orders of magnitude +across sizes with no plausible mechanism). + +### Average power: locating the crossover + +Campaign 1 observed that LAZY draws *more* average power than EAGER at N=10 (a short, +intense burst) but *less* at N=1000 (sustained EAGER memory pressure), and could not say +where the crossover sits. Campaign 2's intermediate sizes locate it, using EnergyTracer's +own reported average power (raw data type): + +| N | EAGER | LAZY | Higher average power | +|---:|---:|---:|:---:| +| 10 | 4.373 W | 5.938 W | LAZY | +| 30 | 4.329 W | 4.613 W | LAZY | +| 50 | 5.623 W | 6.479 W | LAZY | +| 100 | 4.258 W | 3.585 W | **EAGER** | +| 500 | 6.242 W | 4.652 W | EAGER | +| 1000 | 7.826 W | 4.087 W | EAGER | + +**The crossover sits between N=50 and N=100** β€” the same interval where the energy-per-row +anomaly and the correlation collapse occur. That three unrelated statistics all shift +character in the same narrow interval is worth flagging to a reviewer even without a +confirmed mechanism: something about the system's behaviour changes around a collection of +roughly fifty to a hundred elements, on top of the average trend the rule is concerned +with. + +--- + +## 6. What not to claim + +Everything in campaign 1's Β§ 5 still holds (the percentage is not a production figure, the +tool's p-values are not reusable, the absolute yearly saving is modest, level 1 and level 2 +are consistent despite different-looking ratios). Two additions specific to campaign 2: + +### Do not present a single clean exponent as if the curve were smooth + +**N^1.22 is a legitimate summary statistic for energy per read**, and on that metric alone +the fit looks excellent (RΒ² = 0.995) β€” but that is because per-read values are dominated by +the trivial NΒΉ scaling. The same exponent, measured on energy *per row* (RΒ² = 0.85), shows +the fit is visibly imperfect once the trivial factor is removed. Quoting N^1.22 with only +the per-read RΒ² would overstate how well-behaved the relationship is; a reviewer who reruns +the six points and fits their own curve on a per-row basis will find the same kink β€” better +to have already named it. + +### The relevance threshold is not "N β‰ˆ 100" + +Campaign 1 tentatively suggested the rule becomes clearly relevant "around a hundred +elements". With six points, that framing does not survive: the **relative** gap is already +81% at N=10, the smallest size tested β€” there is no size threshold below which the smell +stops mattering in relative terms. What actually grows with N is the **absolute** energy +wasted per parent read (3.3 Β΅J at N=10 versus 936 Β΅J at N=1000, roughly 280 times more) and, +independently, the crossover in average power (between N=50 and N=100). The more accurate +statement is that the relative overhead is present at every size tested; its absolute +weight becomes material as the collection and the read rate grow β€” a statement about +magnitude, not a threshold. + +### The N=100 finding should be disclosed, not smoothed away + +It would be tempting to quietly drop N=100 from a plot or fit and present a cleaner curve +through the remaining five points. That would hide a real observation, and it is disclosed +throughout this document (Β§ 4, Β§ 5) rather than omitted. + +--- + +## Sources + +- Consolidated report: `level2-energytracer/campaign2/consolidated-report/REPORT.md` +- Consolidated data: `level2-energytracer/campaign2/consolidated-report/data/all_measurements.csv` β€” 10,800 + measurements (6 sizes Γ— 2 variants Γ— 900) +- Plots: `level2-energytracer/campaign2/consolidated-report/plots/` (including `scaling_overhead.png`) +- Raw EnergyTracer reports: `level2-energytracer/campaign2/consolidated-report/reports-source/` +- Companion document: `level2-energytracer/campaign1/campaign1-interpretation.md` +- Level 1 (unchanged, not re-run for campaign 2): `level1-jpa/results/level1-20260802-120143.txt` + +Every figure in this document was recomputed independently from the raw +`all_measurements.csv`, using 3Οƒ outlier removal and phase-level aggregation, rather than +taken from `ET-analyzer`'s own per-sample statistics β€” for the same reason given in +campaign 1 Β§ 2 (900 runs within a phase are not independent samples). + +--- + +*Internal analysis, kept as evidence that the code smell targeted by S6904 has a real, +measurable cost.* diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/REPORT.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/REPORT.md new file mode 100644 index 000000000..ba2071001 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/REPORT.md @@ -0,0 +1,811 @@ +# FetchType LAZY on JPA collections - consolidated energy report + +Aggregated across every measurement phase of the campaign. Regenerated by `4-aggregate_report.py`; EnergyTracer's own per-size reports are reproduced verbatim at the end. + +| | | +|---|---| +| Profiler | `mac` | +| Data type | `raw` | +| Collection sizes | 10, 30, 50, 100, 500, 1000 | +| Significance level | Ξ± = 0.05 | +| Host | `M-NGY9VPYVMH` - Darwin 25.5.0 - arm64 | + +## Sample sizes + +| Collection size | Phases | Samples EAGER | Samples LAZY | +|---:|---:|---:|---:| +| 10 | 30 | 900 | 900 | +| 30 | 30 | 900 | 900 | +| 50 | 30 | 900 | 900 | +| 100 | 30 | 900 | 900 | +| 500 | 30 | 900 | 900 | +| 1000 | 30 | 900 | 900 | + +## Effect versus collection size + +An overhead that grows with the number of children is the core argument for the rule: it shows the cost is caused by hydrating the collection, not by incidental noise. + +![scaling](plots/scaling_overhead.png) + +| Collection size | Ξ” cpu_mj | Ξ” dram_mj | Ξ” time_s | +|---:|---:|---:|---:| +| 10 | +79.93% | +86.12% | +86.15% | +| 30 | +83.37% | +85.83% | +84.47% | +| 50 | +85.36% | +87.74% | +87.71% | +| 100 | +86.36% | +87.79% | +84.43% | +| 500 | +87.22% | +86.52% | +82.21% | +| 1000 | +90.61% | +87.31% | +81.02% | + +## Collection size N = 10 + +| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict | +|---|---:|---:|---:|---:|---:|---:|---:|:---:| +| `cpu_mj` | 1592.9822 mJ | 319.7844 mJ | +79.93% | 0.00e+00 | +5.793 (large) | 2.25e-295 | +1.000 (large) | significant | +| `gpu_mj` | 1.4033 mJ | 0.1276 mJ | +90.91% | 1.99e-139 | +1.403 (large) | 8.06e-213 | +0.838 (large) | significant | +| `ane_mj` | 13.2244 mJ | 1.8867 mJ | +85.73% | 4.05e-28 | +0.536 (medium) | 0.0025 | +0.056 (negligible) | significant but negligible | +| `dram_mj` | 454.6222 mJ | 63.1164 mJ | +86.12% | 0.00e+00 | +28.295 (large) | 3.27e-295 | +1.000 (large) | significant | +| `time_s` | 0.4785 s | 0.0663 s | +86.15% | 0.00e+00 | +109.558 (large) | 5.42e-290 | +1.000 (large) | significant | + +> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: positive means the smell costs more. + +> Shapiro-Wilk rejects normality for `cpu_mj`, `gpu_mj`, `ane_mj`, `dram_mj`, `time_s`. For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight than the Welch t-test and Cohen's d. + +![cpu_mj_box](plots/n10/cpu_mj_box.png) +![cpu_mj_violin](plots/n10/cpu_mj_violin.png) +![cpu_mj_per_phase](plots/n10/cpu_mj_per_phase.png) +![gpu_mj_box](plots/n10/gpu_mj_box.png) +![gpu_mj_violin](plots/n10/gpu_mj_violin.png) +![gpu_mj_per_phase](plots/n10/gpu_mj_per_phase.png) +![ane_mj_box](plots/n10/ane_mj_box.png) +![ane_mj_violin](plots/n10/ane_mj_violin.png) +![ane_mj_per_phase](plots/n10/ane_mj_per_phase.png) +![dram_mj_box](plots/n10/dram_mj_box.png) +![dram_mj_violin](plots/n10/dram_mj_violin.png) +![dram_mj_per_phase](plots/n10/dram_mj_per_phase.png) +![time_s_box](plots/n10/time_s_box.png) +![time_s_violin](plots/n10/time_s_violin.png) +![time_s_per_phase](plots/n10/time_s_per_phase.png) + +## Collection size N = 30 + +| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict | +|---|---:|---:|---:|---:|---:|---:|---:|:---:| +| `cpu_mj` | 1672.4042 mJ | 278.0389 mJ | +83.37% | 0.00e+00 | +6.288 (large) | 9.58e-292 | +1.000 (large) | significant | +| `gpu_mj` | 1.8800 mJ | 0.3178 mJ | +83.10% | 1.39e-224 | +1.934 (large) | 3.00e-252 | +0.916 (large) | significant | +| `ane_mj` | 14.5200 mJ | 1.8900 mJ | +86.98% | 5.53e-29 | +0.545 (medium) | 0.1160 | +0.028 (negligible) | borderline | +| `dram_mj` | 474.4535 mJ | 67.2211 mJ | +85.83% | 0.00e+00 | +28.185 (large) | 2.15e-294 | +1.000 (large) | significant | +| `time_s` | 0.4961 s | 0.0770 s | +84.47% | 0.00e+00 | +111.097 (large) | 3.74e-290 | +1.000 (large) | significant | + +> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: positive means the smell costs more. + +> Shapiro-Wilk rejects normality for `cpu_mj`, `gpu_mj`, `ane_mj`, `dram_mj`, `time_s`. For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight than the Welch t-test and Cohen's d. + +![cpu_mj_box](plots/n30/cpu_mj_box.png) +![cpu_mj_violin](plots/n30/cpu_mj_violin.png) +![cpu_mj_per_phase](plots/n30/cpu_mj_per_phase.png) +![gpu_mj_box](plots/n30/gpu_mj_box.png) +![gpu_mj_violin](plots/n30/gpu_mj_violin.png) +![gpu_mj_per_phase](plots/n30/gpu_mj_per_phase.png) +![ane_mj_box](plots/n30/ane_mj_box.png) +![ane_mj_violin](plots/n30/ane_mj_violin.png) +![ane_mj_per_phase](plots/n30/ane_mj_per_phase.png) +![dram_mj_box](plots/n30/dram_mj_box.png) +![dram_mj_violin](plots/n30/dram_mj_violin.png) +![dram_mj_per_phase](plots/n30/dram_mj_per_phase.png) +![time_s_box](plots/n30/time_s_box.png) +![time_s_violin](plots/n30/time_s_violin.png) +![time_s_per_phase](plots/n30/time_s_per_phase.png) + +## Collection size N = 50 + +| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict | +|---|---:|---:|---:|---:|---:|---:|---:|:---:| +| `cpu_mj` | 2247.0589 mJ | 328.8922 mJ | +85.36% | 0.00e+00 | +7.194 (large) | 2.25e-295 | +1.000 (large) | significant | +| `gpu_mj` | 3.4100 mJ | 0.4144 mJ | +87.85% | 0.00e+00 | +3.974 (large) | 8.94e-287 | +0.985 (large) | significant | +| `ane_mj` | 65.5611 mJ | 7.9433 mJ | +87.88% | 7.64e-279 | +2.465 (large) | 1.76e-125 | +0.647 (large) | significant | +| `dram_mj` | 493.3900 mJ | 60.4972 mJ | +87.74% | 0.00e+00 | +31.039 (large) | 2.15e-294 | +1.000 (large) | significant | +| `time_s` | 0.5046 s | 0.0620 s | +87.71% | 0.00e+00 | +113.485 (large) | 2.81e-292 | +1.000 (large) | significant | + +> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: positive means the smell costs more. + +> Shapiro-Wilk rejects normality for `cpu_mj`, `gpu_mj`, `ane_mj`, `dram_mj`, `time_s`. For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight than the Welch t-test and Cohen's d. + +![cpu_mj_box](plots/n50/cpu_mj_box.png) +![cpu_mj_violin](plots/n50/cpu_mj_violin.png) +![cpu_mj_per_phase](plots/n50/cpu_mj_per_phase.png) +![gpu_mj_box](plots/n50/gpu_mj_box.png) +![gpu_mj_violin](plots/n50/gpu_mj_violin.png) +![gpu_mj_per_phase](plots/n50/gpu_mj_per_phase.png) +![ane_mj_box](plots/n50/ane_mj_box.png) +![ane_mj_violin](plots/n50/ane_mj_violin.png) +![ane_mj_per_phase](plots/n50/ane_mj_per_phase.png) +![dram_mj_box](plots/n50/dram_mj_box.png) +![dram_mj_violin](plots/n50/dram_mj_violin.png) +![dram_mj_per_phase](plots/n50/dram_mj_per_phase.png) +![time_s_box](plots/n50/time_s_box.png) +![time_s_violin](plots/n50/time_s_violin.png) +![time_s_per_phase](plots/n50/time_s_per_phase.png) + +## Collection size N = 100 + +| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict | +|---|---:|---:|---:|---:|---:|---:|---:|:---:| +| `cpu_mj` | 1702.9672 mJ | 232.3034 mJ | +86.36% | 0.00e+00 | +81.417 (large) | 8.08e-289 | +1.000 (large) | significant | +| `gpu_mj` | 1.5889 mJ | 0.2867 mJ | +81.96% | 0.00e+00 | +2.519 (large) | 1.65e-256 | +0.921 (large) | significant | +| `ane_mj` | 0.0000 mJ | 0.0000 mJ | +nan% | 1.0000 | +0.000 (negligible) | 1.0000 | +0.000 (negligible) | not significant | +| `dram_mj` | 470.5136 mJ | 57.4355 mJ | +87.79% | 0.00e+00 | +170.568 (large) | 7.45e-288 | +1.000 (large) | significant | +| `time_s` | 0.5119 s | 0.0797 s | +84.43% | 0.00e+00 | +221.489 (large) | 1.75e-290 | +1.000 (large) | significant | + +> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: positive means the smell costs more. + +> Shapiro-Wilk rejects normality for `cpu_mj`, `gpu_mj`, `ane_mj`, `dram_mj`, `time_s`. For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight than the Welch t-test and Cohen's d. + +![cpu_mj_box](plots/n100/cpu_mj_box.png) +![cpu_mj_violin](plots/n100/cpu_mj_violin.png) +![cpu_mj_per_phase](plots/n100/cpu_mj_per_phase.png) +![gpu_mj_box](plots/n100/gpu_mj_box.png) +![gpu_mj_violin](plots/n100/gpu_mj_violin.png) +![gpu_mj_per_phase](plots/n100/gpu_mj_per_phase.png) +![ane_mj_box](plots/n100/ane_mj_box.png) +![ane_mj_violin](plots/n100/ane_mj_violin.png) +![ane_mj_per_phase](plots/n100/ane_mj_per_phase.png) +![dram_mj_box](plots/n100/dram_mj_box.png) +![dram_mj_violin](plots/n100/dram_mj_violin.png) +![dram_mj_per_phase](plots/n100/dram_mj_per_phase.png) +![time_s_box](plots/n100/time_s_box.png) +![time_s_violin](plots/n100/time_s_violin.png) +![time_s_per_phase](plots/n100/time_s_per_phase.png) + +## Collection size N = 500 + +| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict | +|---|---:|---:|---:|---:|---:|---:|---:|:---:| +| `cpu_mj` | 3185.1278 mJ | 407.1578 mJ | +87.22% | 0.00e+00 | +8.263 (large) | 2.25e-295 | +1.000 (large) | significant | +| `gpu_mj` | 2.7822 mJ | 0.4244 mJ | +84.75% | 3.81e-273 | +2.269 (large) | 8.26e-279 | +0.967 (large) | significant | +| `ane_mj` | 28.0478 mJ | 5.1622 mJ | +81.59% | 2.97e-43 | +0.684 (medium) | 1.01e-05 | +0.096 (negligible) | significant but negligible | +| `dram_mj` | 599.8467 mJ | 80.8667 mJ | +86.52% | 0.00e+00 | +19.310 (large) | 2.25e-295 | +1.000 (large) | significant | +| `time_s` | 0.6141 s | 0.1093 s | +82.21% | 0.00e+00 | +92.123 (large) | 1.93e-290 | +1.000 (large) | significant | + +> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: positive means the smell costs more. + +> Shapiro-Wilk rejects normality for `cpu_mj`, `gpu_mj`, `ane_mj`, `dram_mj`, `time_s`. For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight than the Welch t-test and Cohen's d. + +![cpu_mj_box](plots/n500/cpu_mj_box.png) +![cpu_mj_violin](plots/n500/cpu_mj_violin.png) +![cpu_mj_per_phase](plots/n500/cpu_mj_per_phase.png) +![gpu_mj_box](plots/n500/gpu_mj_box.png) +![gpu_mj_violin](plots/n500/gpu_mj_violin.png) +![gpu_mj_per_phase](plots/n500/gpu_mj_per_phase.png) +![ane_mj_box](plots/n500/ane_mj_box.png) +![ane_mj_violin](plots/n500/ane_mj_violin.png) +![ane_mj_per_phase](plots/n500/ane_mj_per_phase.png) +![dram_mj_box](plots/n500/dram_mj_box.png) +![dram_mj_violin](plots/n500/dram_mj_violin.png) +![dram_mj_per_phase](plots/n500/dram_mj_per_phase.png) +![time_s_box](plots/n500/time_s_box.png) +![time_s_violin](plots/n500/time_s_violin.png) +![time_s_per_phase](plots/n500/time_s_per_phase.png) + +## Collection size N = 1000 + +| Metric | Mean EAGER | Mean LAZY | Ξ” mean | Welch p | Cohen's d | Mann-Whitney p | Cliff's Ξ΄ | Verdict | +|---|---:|---:|---:|---:|---:|---:|---:|:---:| +| `cpu_mj` | 4500.1731 mJ | 422.5982 mJ | +90.61% | 0.00e+00 | +12.775 (large) | 6.41e-292 | +1.000 (large) | significant | +| `gpu_mj` | 2.8011 mJ | 0.4189 mJ | +85.05% | 1.48e-302 | +2.504 (large) | 2.18e-298 | +1.000 (large) | significant | +| `ane_mj` | 22.0711 mJ | 4.1422 mJ | +81.23% | 1.02e-31 | +0.573 (medium) | 6.79e-04 | +0.067 (negligible) | significant but negligible | +| `dram_mj` | 689.2260 mJ | 87.4400 mJ | +87.31% | 0.00e+00 | +24.607 (large) | 3.27e-295 | +1.000 (large) | significant | +| `time_s` | 0.6708 s | 0.1273 s | +81.02% | 0.00e+00 | +97.082 (large) | 5.79e-290 | +1.000 (large) | significant | + +> Ξ” mean = (EAGER βˆ’ LAZY) / EAGER Γ— 100, EnergyTracer's convention: positive means the smell costs more. + +> Shapiro-Wilk rejects normality for `cpu_mj`, `gpu_mj`, `ane_mj`, `dram_mj`, `time_s`. For those metrics the Mann-Whitney result and Cliff's Ξ΄ carry more weight than the Welch t-test and Cohen's d. + +![cpu_mj_box](plots/n1000/cpu_mj_box.png) +![cpu_mj_violin](plots/n1000/cpu_mj_violin.png) +![cpu_mj_per_phase](plots/n1000/cpu_mj_per_phase.png) +![gpu_mj_box](plots/n1000/gpu_mj_box.png) +![gpu_mj_violin](plots/n1000/gpu_mj_violin.png) +![gpu_mj_per_phase](plots/n1000/gpu_mj_per_phase.png) +![ane_mj_box](plots/n1000/ane_mj_box.png) +![ane_mj_violin](plots/n1000/ane_mj_violin.png) +![ane_mj_per_phase](plots/n1000/ane_mj_per_phase.png) +![dram_mj_box](plots/n1000/dram_mj_box.png) +![dram_mj_violin](plots/n1000/dram_mj_violin.png) +![dram_mj_per_phase](plots/n1000/dram_mj_per_phase.png) +![time_s_box](plots/n1000/time_s_box.png) +![time_s_violin](plots/n1000/time_s_violin.png) +![time_s_per_phase](plots/n1000/time_s_per_phase.png) + +## EnergyTracer reports, verbatim + +Unmodified output of `ET-analyzer`, kept so every number above can be traced back to the tool itself. + +
N = 10 - cleaned_mac_report.md + +## Energy Report β€” `mac` (cleaned) + +> 890 samples (with smell) vs 875 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 473.04 ms | 65.11 ms | +| **Average Power** | 4.361 W | 5.933 W | +| **Total Energy** | 1835.92 J | 337.99 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.86% | 0.00e+00 | +5.782 | large | βœ… | +| `gpu_mj` | +90.68% | 4.91e-137 | +1.397 | large | βœ… | +| `ane_mj` | +85.47% | 8.58e-28 | +0.535 | medium | βœ… | +| `dram_mj` | +86.08% | 0.00e+00 | +28.390 | large | βœ… | +| `time_s` | +86.12% | 0.00e+00 | +111.194 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.9% lower energy (Cohen’s d = +5.782, large) +- **`gpu_mj`**: 90.7% lower energy (Cohen’s d = +1.397, large) +- **`ane_mj`**: 85.5% lower energy (Cohen’s d = +0.535, medium) +- **`dram_mj`**: 86.1% lower energy (Cohen’s d = +28.390, large) +- **`time_s`**: 86.1% lower time (Cohen’s d = +111.194, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 10 - raw_mac_report.md + +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 471.54 ms | 64.80 ms | +| **Average Power** | 4.373 W | 5.938 W | +| **Total Energy** | 1856.01 J | 346.36 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.93% | 0.00e+00 | +5.793 | large | βœ… | +| `gpu_mj` | +90.91% | 1.99e-139 | +1.403 | large | βœ… | +| `ane_mj` | +85.73% | 4.05e-28 | +0.536 | medium | βœ… | +| `dram_mj` | +86.12% | 0.00e+00 | +28.295 | large | βœ… | +| `time_s` | +86.15% | 0.00e+00 | +109.558 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.9% lower energy (Cohen’s d = +5.793, large) +- **`gpu_mj`**: 90.9% lower energy (Cohen’s d = +1.403, large) +- **`ane_mj`**: 85.7% lower energy (Cohen’s d = +0.536, medium) +- **`dram_mj`**: 86.1% lower energy (Cohen’s d = +28.295, large) +- **`time_s`**: 86.1% lower time (Cohen’s d = +109.558, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 30 - cleaned_mac_report.md + +## Energy Report β€” `mac` (cleaned) + +> 898 samples (with smell) vs 881 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 490.02 ms | 75.55 ms | +| **Average Power** | 4.325 W | 4.612 W | +| **Total Energy** | 1903.20 J | 307.00 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +83.33% | 0.00e+00 | +6.279 | large | βœ… | +| `gpu_mj` | +83.08% | 8.41e-224 | +1.932 | large | βœ… | +| `ane_mj` | +86.82% | 6.94e-29 | +0.545 | medium | βœ… | +| `dram_mj` | +85.80% | 0.00e+00 | +28.191 | large | βœ… | +| `time_s` | +84.45% | 0.00e+00 | +112.566 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 83.3% lower energy (Cohen’s d = +6.279, large) +- **`gpu_mj`**: 83.1% lower energy (Cohen’s d = +1.932, large) +- **`ane_mj`**: 86.8% lower energy (Cohen’s d = +0.545, medium) +- **`dram_mj`**: 85.8% lower energy (Cohen’s d = +28.191, large) +- **`time_s`**: 84.5% lower time (Cohen’s d = +112.566, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 30 - raw_mac_report.md + +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 489.50 ms | 75.33 ms | +| **Average Power** | 4.329 W | 4.613 W | +| **Total Energy** | 1907.29 J | 312.72 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +83.37% | 0.00e+00 | +6.288 | large | βœ… | +| `gpu_mj` | +83.10% | 1.39e-224 | +1.934 | large | βœ… | +| `ane_mj` | +86.98% | 5.53e-29 | +0.545 | medium | βœ… | +| `dram_mj` | +85.83% | 0.00e+00 | +28.185 | large | βœ… | +| `time_s` | +84.47% | 0.00e+00 | +111.097 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 83.4% lower energy (Cohen’s d = +6.288, large) +- **`gpu_mj`**: 83.1% lower energy (Cohen’s d = +1.934, large) +- **`ane_mj`**: 87.0% lower energy (Cohen’s d = +0.545, medium) +- **`dram_mj`**: 85.8% lower energy (Cohen’s d = +28.185, large) +- **`time_s`**: 84.5% lower time (Cohen’s d = +111.097, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 50 - cleaned_mac_report.md + +## Energy Report β€” `mac` (cleaned) + +> 891 samples (with smell) vs 890 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 500.03 ms | 61.56 ms | +| **Average Power** | 5.624 W | 6.458 W | +| **Total Energy** | 2505.74 J | 353.85 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +85.38% | 0.00e+00 | +7.249 | large | βœ… | +| `gpu_mj` | +87.81% | 0.00e+00 | +3.995 | large | βœ… | +| `ane_mj` | +87.93% | 1.45e-279 | +2.494 | large | βœ… | +| `dram_mj` | +87.72% | 0.00e+00 | +31.230 | large | βœ… | +| `time_s` | +87.70% | 0.00e+00 | +115.051 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 85.4% lower energy (Cohen’s d = +7.249, large) +- **`gpu_mj`**: 87.8% lower energy (Cohen’s d = +3.995, large) +- **`ane_mj`**: 87.9% lower energy (Cohen’s d = +2.494, large) +- **`dram_mj`**: 87.7% lower energy (Cohen’s d = +31.230, large) +- **`time_s`**: 87.7% lower time (Cohen’s d = +115.051, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 50 - raw_mac_report.md + +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 499.59 ms | 61.33 ms | +| **Average Power** | 5.623 W | 6.479 W | +| **Total Energy** | 2528.48 J | 357.61 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +85.36% | 0.00e+00 | +7.194 | large | βœ… | +| `gpu_mj` | +87.85% | 0.00e+00 | +3.974 | large | βœ… | +| `ane_mj` | +87.88% | 7.64e-279 | +2.465 | large | βœ… | +| `dram_mj` | +87.74% | 0.00e+00 | +31.039 | large | βœ… | +| `time_s` | +87.71% | 0.00e+00 | +113.485 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 85.4% lower energy (Cohen’s d = +7.194, large) +- **`gpu_mj`**: 87.8% lower energy (Cohen’s d = +3.974, large) +- **`ane_mj`**: 87.9% lower energy (Cohen’s d = +2.465, large) +- **`dram_mj`**: 87.7% lower energy (Cohen’s d = +31.039, large) +- **`time_s`**: 87.7% lower time (Cohen’s d = +113.485, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 100 - cleaned_mac_report.md + +## Energy Report β€” `mac` (cleaned) + +> 887 samples (with smell) vs 872 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 505.49 ms | 77.68 ms | +| **Average Power** | 4.241 W | 3.619 W | +| **Total Energy** | 1901.70 J | 245.10 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.32% | 0.00e+00 | +84.860 | large | βœ… | +| `gpu_mj` | +81.92% | 0.00e+00 | +2.516 | large | βœ… | +| `dram_mj` | +87.77% | 0.00e+00 | +177.673 | large | βœ… | +| `time_s` | +84.39% | 0.00e+00 | +250.581 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.3% lower energy (Cohen’s d = +84.860, large) +- **`gpu_mj`**: 81.9% lower energy (Cohen’s d = +2.516, large) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +177.673, large) +- **`time_s`**: 84.4% lower time (Cohen’s d = +250.581, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 100 - raw_mac_report.md + +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 504.53 ms | 78.18 ms | +| **Average Power** | 4.258 W | 3.585 W | +| **Total Energy** | 1933.48 J | 252.27 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.36% | 0.00e+00 | +81.417 | large | βœ… | +| `gpu_mj` | +81.96% | 0.00e+00 | +2.519 | large | βœ… | +| `dram_mj` | +87.79% | 0.00e+00 | +170.568 | large | βœ… | +| `time_s` | +84.43% | 0.00e+00 | +221.489 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.4% lower energy (Cohen’s d = +81.417, large) +- **`gpu_mj`**: 82.0% lower energy (Cohen’s d = +2.519, large) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +170.568, large) +- **`time_s`**: 84.4% lower time (Cohen’s d = +221.489, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 500 - cleaned_mac_report.md + +## Energy Report β€” `mac` (cleaned) + +> 875 samples (with smell) vs 877 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 613.25 ms | 108.37 ms | +| **Average Power** | 6.227 W | 4.551 W | +| **Total Energy** | 3341.35 J | 432.52 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +87.24% | 0.00e+00 | +8.245 | large | βœ… | +| `gpu_mj` | +84.73% | 1.53e-265 | +2.269 | large | βœ… | +| `ane_mj` | +81.57% | 7.15e-43 | +0.691 | medium | βœ… | +| `dram_mj` | +86.54% | 0.00e+00 | +19.272 | large | βœ… | +| `time_s` | +82.21% | 0.00e+00 | +92.465 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 87.2% lower energy (Cohen’s d = +8.245, large) +- **`gpu_mj`**: 84.7% lower energy (Cohen’s d = +2.269, large) +- **`ane_mj`**: 81.6% lower energy (Cohen’s d = +0.691, medium) +- **`dram_mj`**: 86.5% lower energy (Cohen’s d = +19.272, large) +- **`time_s`**: 82.2% lower time (Cohen’s d = +92.465, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 500 - raw_mac_report.md + +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 611.35 ms | 106.11 ms | +| **Average Power** | 6.242 W | 4.652 W | +| **Total Energy** | 3434.22 J | 444.25 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +87.22% | 0.00e+00 | +8.263 | large | βœ… | +| `gpu_mj` | +84.75% | 3.81e-273 | +2.269 | large | βœ… | +| `ane_mj` | +81.59% | 2.97e-43 | +0.684 | medium | βœ… | +| `dram_mj` | +86.52% | 0.00e+00 | +19.310 | large | βœ… | +| `time_s` | +82.21% | 0.00e+00 | +92.123 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 87.2% lower energy (Cohen’s d = +8.263, large) +- **`gpu_mj`**: 84.7% lower energy (Cohen’s d = +2.269, large) +- **`ane_mj`**: 81.6% lower energy (Cohen’s d = +0.684, medium) +- **`dram_mj`**: 86.5% lower energy (Cohen’s d = +19.310, large) +- **`time_s`**: 82.2% lower time (Cohen’s d = +92.123, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 1000 - cleaned_mac_report.md + +## Energy Report β€” `mac` (cleaned) + +> 883 samples (with smell) vs 876 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 667.61 ms | 125.92 ms | +| **Average Power** | 7.811 W | 4.028 W | +| **Total Energy** | 4604.63 J | 444.29 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +90.58% | 0.00e+00 | +12.693 | large | βœ… | +| `gpu_mj` | +85.03% | 2.06e-296 | +2.501 | large | βœ… | +| `ane_mj` | +81.14% | 2.51e-31 | +0.575 | medium | βœ… | +| `dram_mj` | +87.28% | 0.00e+00 | +24.469 | large | βœ… | +| `time_s` | +81.01% | 0.00e+00 | +98.450 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 90.6% lower energy (Cohen’s d = +12.693, large) +- **`gpu_mj`**: 85.0% lower energy (Cohen’s d = +2.501, large) +- **`ane_mj`**: 81.1% lower energy (Cohen’s d = +0.575, medium) +- **`dram_mj`**: 87.3% lower energy (Cohen’s d = +24.469, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +98.450, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +
N = 1000 - raw_mac_report.md + +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 665.57 ms | 123.63 ms | +| **Average Power** | 7.826 W | 4.087 W | +| **Total Energy** | 4687.65 J | 454.69 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +90.61% | 0.00e+00 | +12.775 | large | βœ… | +| `gpu_mj` | +85.05% | 1.48e-302 | +2.504 | large | βœ… | +| `ane_mj` | +81.23% | 1.02e-31 | +0.573 | medium | βœ… | +| `dram_mj` | +87.31% | 0.00e+00 | +24.607 | large | βœ… | +| `time_s` | +81.02% | 0.00e+00 | +97.082 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 90.6% lower energy (Cohen’s d = +12.775, large) +- **`gpu_mj`**: 85.0% lower energy (Cohen’s d = +2.504, large) +- **`ane_mj`**: 81.2% lower energy (Cohen’s d = +0.573, medium) +- **`dram_mj`**: 87.3% lower energy (Cohen’s d = +24.607, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +97.082, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. + + +
+ +## How to read this, and what not to claim + +- A result is worth quoting only when Welch **and** Mann-Whitney agree and Cliff's Ξ΄ is at least small. Large sample sizes make tiny differences "significant" without making them relevant. +- Check the per-phase plots before trusting anything: a visible trend means the machine drifted during the campaign and the run should be repeated. +- These figures measure the client side only - no database engine, no network. They are a lower bound on the real saving. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/data/all_measurements.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/data/all_measurements.csv new file mode 100644 index 000000000..23b807c7f --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/data/all_measurements.csv @@ -0,0 +1,10801 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s,size,profiler,phase,variant +0,290.8686257733098,0.0,0.0,61.83024365562761,0.0671084160021564,10,mac,phase-19,without +1,288.43744237162144,0.0,0.0,61.31344449346887,0.0665474999987054,10,mac,phase-19,without +2,286.20184035980265,0.0,0.0,60.83822026899203,0.0660317080000822,10,mac,phase-19,without +3,288.09936626467214,0.0,0.0,61.24157930687046,0.0664695000014035,10,mac,phase-19,without +4,285.997408337104,0.0,0.0,60.79476394316497,0.065984542001388,10,mac,phase-19,without 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/data/n10/with_smell.csv new file mode 100644 index 000000000..f4bab90b3 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/data/n10/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s,size,profiler,phase,variant +0,1386.7005541149863,0.0998895877817119,0.0,439.2811105347087,0.4755048749975685,10,mac,phase-19,with +1,1389.776976809194,0.1001111948142143,0.0,440.255664394643,0.4765597919977153,10,mac,phase-19,with +2,1385.844628906725,0.0998279320652189,0.0,439.009969245478,0.4752113750000717,10,mac,phase-19,with +3,1385.5003865740737,0.0998031349130122,0.0,438.90091963579016,0.4750933329996769,10,mac,phase-19,with +4,1389.920116092038,0.1001215057093215,0.0,440.3010082743598,0.4766088750002382,10,mac,phase-19,with +5,1395.4414636433553,0.1005192304590982,0.0,442.0500691489612,0.4785021659990889,10,mac,phase-19,with 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a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/data/n30/without_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/data/n30/without_smell.csv new file mode 100644 index 000000000..3e4ee8bcc --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/data/n30/without_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s,size,profiler,phase,variant +0,254.7701558196424,0.0,0.0,63.11246118665094,0.0760953749995678,30,mac,phase-19,without +1,253.09222999705185,0.0,0.0,62.69680014498916,0.0755942079995293,30,mac,phase-19,without +2,263.90234613131526,0.0,0.0,65.374715981528,0.0788229999998293,30,mac,phase-19,without +3,259.7544057697603,0.0,0.0,64.3471751997949,0.0775840829992375,30,mac,phase-19,without +4,258.13381843399344,0.0,0.0,63.9457182277288,0.0771000419990741,30,mac,phase-19,without 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000000000..ff06af2ab --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/data/n50/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s,size,profiler,phase,variant +0,2465.383134850831,3.173907289853979,80.05674664089132,513.1937967924534,0.513869665999664,50,mac,phase-19,with +1,2464.067773614059,3.172213908180694,80.0140337903875,512.9199913876422,0.5135954999968817,50,mac,phase-19,with +2,2412.3763506907044,3.1056669355336664,78.33549259734386,502.1599122678319,0.5028212500001246,50,mac,phase-19,with +3,2431.3447868159506,3.130086692789243,78.95144200641803,506.10837918833727,0.5067749169975286,50,mac,phase-19,with +4,2450.686837645002,3.1549874375291744,79.57952355725183,510.1346176915523,0.5108064580017526,50,mac,phase-19,with +5,2443.362178479309,3.145557751411404,79.34167477230254,508.6099176989567,0.5092797500001325,50,mac,phase-19,with 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n10/cleaned_mac_report.md new file mode 100644 index 000000000..6f25ee71a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n10/cleaned_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 890 samples (with smell) vs 875 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 473.04 ms | 65.11 ms | +| **Average Power** | 4.361 W | 5.933 W | +| **Total Energy** | 1835.92 J | 337.99 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.86% | 0.00e+00 | +5.782 | large | βœ… | +| `gpu_mj` | +90.68% | 4.91e-137 | +1.397 | large | βœ… | +| `ane_mj` | +85.47% | 8.58e-28 | +0.535 | medium | βœ… | +| `dram_mj` | +86.08% | 0.00e+00 | +28.390 | large | βœ… | +| `time_s` | +86.12% | 0.00e+00 | +111.194 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.9% lower energy (Cohen’s d = +5.782, large) +- **`gpu_mj`**: 90.7% lower energy (Cohen’s d = +1.397, large) +- **`ane_mj`**: 85.5% lower energy (Cohen’s d = +0.535, medium) +- **`dram_mj`**: 86.1% lower energy (Cohen’s d = +28.390, large) +- **`time_s`**: 86.1% lower time (Cohen’s d = +111.194, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n10/raw_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n10/raw_mac_report.md new file mode 100644 index 000000000..31742836b --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n10/raw_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 471.54 ms | 64.80 ms | +| **Average Power** | 4.373 W | 5.938 W | +| **Total Energy** | 1856.01 J | 346.36 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.93% | 0.00e+00 | +5.793 | large | βœ… | +| `gpu_mj` | +90.91% | 1.99e-139 | +1.403 | large | βœ… | +| `ane_mj` | +85.73% | 4.05e-28 | +0.536 | medium | βœ… | +| `dram_mj` | +86.12% | 0.00e+00 | +28.295 | large | βœ… | +| `time_s` | +86.15% | 0.00e+00 | +109.558 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.9% lower energy (Cohen’s d = +5.793, large) +- **`gpu_mj`**: 90.9% lower energy (Cohen’s d = +1.403, large) +- **`ane_mj`**: 85.7% lower energy (Cohen’s d = +0.536, medium) +- **`dram_mj`**: 86.1% lower energy (Cohen’s d = +28.295, large) +- **`time_s`**: 86.1% lower time (Cohen’s d = +109.558, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n100/cleaned_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n100/cleaned_mac_report.md new file mode 100644 index 000000000..5290d4029 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n100/cleaned_mac_report.md @@ -0,0 +1,41 @@ +## Energy Report β€” `mac` (cleaned) + +> 887 samples (with smell) vs 872 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 505.49 ms | 77.68 ms | +| **Average Power** | 4.241 W | 3.619 W | +| **Total Energy** | 1901.70 J | 245.10 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.32% | 0.00e+00 | +84.860 | large | βœ… | +| `gpu_mj` | +81.92% | 0.00e+00 | +2.516 | large | βœ… | +| `dram_mj` | +87.77% | 0.00e+00 | +177.673 | large | βœ… | +| `time_s` | +84.39% | 0.00e+00 | +250.581 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.3% lower energy (Cohen’s d = +84.860, large) +- **`gpu_mj`**: 81.9% lower energy (Cohen’s d = +2.516, large) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +177.673, large) +- **`time_s`**: 84.4% lower time (Cohen’s d = +250.581, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n100/raw_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n100/raw_mac_report.md new file mode 100644 index 000000000..cfd12a070 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n100/raw_mac_report.md @@ -0,0 +1,41 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 504.53 ms | 78.18 ms | +| **Average Power** | 4.258 W | 3.585 W | +| **Total Energy** | 1933.48 J | 252.27 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.36% | 0.00e+00 | +81.417 | large | βœ… | +| `gpu_mj` | +81.96% | 0.00e+00 | +2.519 | large | βœ… | +| `dram_mj` | +87.79% | 0.00e+00 | +170.568 | large | βœ… | +| `time_s` | +84.43% | 0.00e+00 | +221.489 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.4% lower energy (Cohen’s d = +81.417, large) +- **`gpu_mj`**: 82.0% lower energy (Cohen’s d = +2.519, large) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +170.568, large) +- **`time_s`**: 84.4% lower time (Cohen’s d = +221.489, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n1000/cleaned_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n1000/cleaned_mac_report.md new file mode 100644 index 000000000..ad3eea23d --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n1000/cleaned_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 883 samples (with smell) vs 876 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 667.61 ms | 125.92 ms | +| **Average Power** | 7.811 W | 4.028 W | +| **Total Energy** | 4604.63 J | 444.29 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +90.58% | 0.00e+00 | +12.693 | large | βœ… | +| `gpu_mj` | +85.03% | 2.06e-296 | +2.501 | large | βœ… | +| `ane_mj` | +81.14% | 2.51e-31 | +0.575 | medium | βœ… | +| `dram_mj` | +87.28% | 0.00e+00 | +24.469 | large | βœ… | +| `time_s` | +81.01% | 0.00e+00 | +98.450 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 90.6% lower energy (Cohen’s d = +12.693, large) +- **`gpu_mj`**: 85.0% lower energy (Cohen’s d = +2.501, large) +- **`ane_mj`**: 81.1% lower energy (Cohen’s d = +0.575, medium) +- **`dram_mj`**: 87.3% lower energy (Cohen’s d = +24.469, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +98.450, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n1000/raw_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n1000/raw_mac_report.md new file mode 100644 index 000000000..3003b419e --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n1000/raw_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 665.57 ms | 123.63 ms | +| **Average Power** | 7.826 W | 4.087 W | +| **Total Energy** | 4687.65 J | 454.69 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +90.61% | 0.00e+00 | +12.775 | large | βœ… | +| `gpu_mj` | +85.05% | 1.48e-302 | +2.504 | large | βœ… | +| `ane_mj` | +81.23% | 1.02e-31 | +0.573 | medium | βœ… | +| `dram_mj` | +87.31% | 0.00e+00 | +24.607 | large | βœ… | +| `time_s` | +81.02% | 0.00e+00 | +97.082 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 90.6% lower energy (Cohen’s d = +12.775, large) +- **`gpu_mj`**: 85.0% lower energy (Cohen’s d = +2.504, large) +- **`ane_mj`**: 81.2% lower energy (Cohen’s d = +0.573, medium) +- **`dram_mj`**: 87.3% lower energy (Cohen’s d = +24.607, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +97.082, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n30/cleaned_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n30/cleaned_mac_report.md new file mode 100644 index 000000000..e935a0298 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n30/cleaned_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 898 samples (with smell) vs 881 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 490.02 ms | 75.55 ms | +| **Average Power** | 4.325 W | 4.612 W | +| **Total Energy** | 1903.20 J | 307.00 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +83.33% | 0.00e+00 | +6.279 | large | βœ… | +| `gpu_mj` | +83.08% | 8.41e-224 | +1.932 | large | βœ… | +| `ane_mj` | +86.82% | 6.94e-29 | +0.545 | medium | βœ… | +| `dram_mj` | +85.80% | 0.00e+00 | +28.191 | large | βœ… | +| `time_s` | +84.45% | 0.00e+00 | +112.566 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 83.3% lower energy (Cohen’s d = +6.279, large) +- **`gpu_mj`**: 83.1% lower energy (Cohen’s d = +1.932, large) +- **`ane_mj`**: 86.8% lower energy (Cohen’s d = +0.545, medium) +- **`dram_mj`**: 85.8% lower energy (Cohen’s d = +28.191, large) +- **`time_s`**: 84.5% lower time (Cohen’s d = +112.566, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n30/raw_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n30/raw_mac_report.md new file mode 100644 index 000000000..abd4a7433 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n30/raw_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 489.50 ms | 75.33 ms | +| **Average Power** | 4.329 W | 4.613 W | +| **Total Energy** | 1907.29 J | 312.72 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +83.37% | 0.00e+00 | +6.288 | large | βœ… | +| `gpu_mj` | +83.10% | 1.39e-224 | +1.934 | large | βœ… | +| `ane_mj` | +86.98% | 5.53e-29 | +0.545 | medium | βœ… | +| `dram_mj` | +85.83% | 0.00e+00 | +28.185 | large | βœ… | +| `time_s` | +84.47% | 0.00e+00 | +111.097 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 83.4% lower energy (Cohen’s d = +6.288, large) +- **`gpu_mj`**: 83.1% lower energy (Cohen’s d = +1.934, large) +- **`ane_mj`**: 87.0% lower energy (Cohen’s d = +0.545, medium) +- **`dram_mj`**: 85.8% lower energy (Cohen’s d = +28.185, large) +- **`time_s`**: 84.5% lower time (Cohen’s d = +111.097, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n50/cleaned_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n50/cleaned_mac_report.md new file mode 100644 index 000000000..9ad8b0ac5 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n50/cleaned_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 891 samples (with smell) vs 890 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 500.03 ms | 61.56 ms | +| **Average Power** | 5.624 W | 6.458 W | +| **Total Energy** | 2505.74 J | 353.85 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +85.38% | 0.00e+00 | +7.249 | large | βœ… | +| `gpu_mj` | +87.81% | 0.00e+00 | +3.995 | large | βœ… | +| `ane_mj` | +87.93% | 1.45e-279 | +2.494 | large | βœ… | +| `dram_mj` | +87.72% | 0.00e+00 | +31.230 | large | βœ… | +| `time_s` | +87.70% | 0.00e+00 | +115.051 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 85.4% lower energy (Cohen’s d = +7.249, large) +- **`gpu_mj`**: 87.8% lower energy (Cohen’s d = +3.995, large) +- **`ane_mj`**: 87.9% lower energy (Cohen’s d = +2.494, large) +- **`dram_mj`**: 87.7% lower energy (Cohen’s d = +31.230, large) +- **`time_s`**: 87.7% lower time (Cohen’s d = +115.051, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n50/raw_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n50/raw_mac_report.md new file mode 100644 index 000000000..73643e782 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n50/raw_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 499.59 ms | 61.33 ms | +| **Average Power** | 5.623 W | 6.479 W | +| **Total Energy** | 2528.48 J | 357.61 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +85.36% | 0.00e+00 | +7.194 | large | βœ… | +| `gpu_mj` | +87.85% | 0.00e+00 | +3.974 | large | βœ… | +| `ane_mj` | +87.88% | 7.64e-279 | +2.465 | large | βœ… | +| `dram_mj` | +87.74% | 0.00e+00 | +31.039 | large | βœ… | +| `time_s` | +87.71% | 0.00e+00 | +113.485 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 85.4% lower energy (Cohen’s d = +7.194, large) +- **`gpu_mj`**: 87.8% lower energy (Cohen’s d = +3.974, large) +- **`ane_mj`**: 87.9% lower energy (Cohen’s d = +2.465, large) +- **`dram_mj`**: 87.7% lower energy (Cohen’s d = +31.039, large) +- **`time_s`**: 87.7% lower time (Cohen’s d = +113.485, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n500/cleaned_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n500/cleaned_mac_report.md new file mode 100644 index 000000000..20c6d52c2 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n500/cleaned_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 875 samples (with smell) vs 877 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 613.25 ms | 108.37 ms | +| **Average Power** | 6.227 W | 4.551 W | +| **Total Energy** | 3341.35 J | 432.52 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +87.24% | 0.00e+00 | +8.245 | large | βœ… | +| `gpu_mj` | +84.73% | 1.53e-265 | +2.269 | large | βœ… | +| `ane_mj` | +81.57% | 7.15e-43 | +0.691 | medium | βœ… | +| `dram_mj` | +86.54% | 0.00e+00 | +19.272 | large | βœ… | +| `time_s` | +82.21% | 0.00e+00 | +92.465 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 87.2% lower energy (Cohen’s d = +8.245, large) +- **`gpu_mj`**: 84.7% lower energy (Cohen’s d = +2.269, large) +- **`ane_mj`**: 81.6% lower energy (Cohen’s d = +0.691, medium) +- **`dram_mj`**: 86.5% lower energy (Cohen’s d = +19.272, large) +- **`time_s`**: 82.2% lower time (Cohen’s d = +92.465, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n500/raw_mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n500/raw_mac_report.md new file mode 100644 index 000000000..f567e5107 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/consolidated-report/reports-source/n500/raw_mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 611.35 ms | 106.11 ms | +| **Average Power** | 6.242 W | 4.652 W | +| **Total Energy** | 3434.22 J | 444.25 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +87.22% | 0.00e+00 | +8.263 | large | βœ… | +| `gpu_mj` | +84.75% | 3.81e-273 | +2.269 | large | βœ… | +| `ane_mj` | +81.59% | 2.97e-43 | +0.684 | medium | βœ… | +| `dram_mj` | +86.52% | 0.00e+00 | +19.310 | large | βœ… | +| `time_s` | +82.21% | 0.00e+00 | +92.123 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 87.2% lower energy (Cohen’s d = +8.263, large) +- **`gpu_mj`**: 84.7% lower energy (Cohen’s d = +2.269, large) +- **`ane_mj`**: 81.6% lower energy (Cohen’s d = +0.684, medium) +- **`dram_mj`**: 86.5% lower energy (Cohen’s d = +19.310, large) +- **`time_s`**: 82.2% lower time (Cohen’s d = +92.123, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-1/cleaned/csv/history_with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-1/cleaned/csv/history_with_smell.csv new file mode 100644 index 000000000..34d3c21e3 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-1/cleaned/csv/history_with_smell.csv @@ -0,0 +1,31 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1398.2044033134116,0.40261200844077694,0.0,442.1350872693799,0.4791000420009368 +1,1375.8131175329577,0.3961644529057804,0.0,435.05459669936454,0.47142758300105925 +2,1392.2357928925342,0.40089335112325214,0.0,440.2477150918514,0.477054875002068 +3,1393.0715489932206,0.40113400652489917,0.0,440.5119948320934,0.47734124999988126 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+15,1387.6137807153643,0.39956244585555434,0.0,438.7861592903746,0.4754711249988759 +16,1380.8976957133325,0.3976285537399815,0.0,436.6624234321231,0.4731698329997016 +17,1387.905133100111,0.3996463405768904,0.0,438.87828967685516,0.4755709579985705 +18,1389.2666881497526,0.40003839942882924,0.0,439.308835639426,0.4760375000005297 +19,1390.355250444137,0.4003518502022758,0.0,439.6530568304659,0.47641049999947427 +20,1387.9581516296382,0.39966160722646393,0.0,438.89505500252847,0.47558912499880535 +21,1383.6377091625147,0.39841753875198394,0.0,437.5288604728037,0.4741087090005749 +22,1386.509896833805,0.3992445832414854,0.0,438.4370931630245,0.475092874999973 +23,1385.2003855818612,0.39886751036575163,0.0,438.0230042999896,0.4746441660026903 +24,1383.1030324544151,0.3982635789569751,0.0,437.3597869579182,0.473925499998586 +25,1386.0615580103492,0.3991154843817294,0.0,438.29532109853585,0.4749392500016256 +26,1388.7087868377796,0.39987775212490656,0.0,439.1324181251682,0.47584633299993584 +27,1386.8714133370133,0.39934868167308535,0.0,438.5514105906599,0.47521675000098185 +28,1388.1705847108346,0.39972277718793525,0.0,438.9622298152176,0.4756619159998081 +29,1389.9437563946433,0.40023336077016175,0.0,439.52293568576596,0.47626950000267243 diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-1/cleaned/csv/history_without_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-1/cleaned/csv/history_without_smell.csv new file mode 100644 index 000000000..f2ac3b2e2 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-1/cleaned/csv/history_without_smell.csv @@ -0,0 +1,30 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,285.85022831842826,0.0,0.0,60.64892627672344,0.0659001249987341 +1,290.22725500807564,0.0,0.0,61.57760130550795,0.06690920799883315 +2,287.38755742396035,0.0,0.0,60.97510184122435,0.06625454199820524 +3,284.85600928125524,0.0,0.0,60.43798253376007,0.06567091699980665 +4,288.39804257958247,0.0,0.0,61.18949677128115,0.06648750000022119 +5,289.7942165918819,0.0,0.0,61.485723418499695,0.06680937499913853 +6,287.9792792941568,0.0,0.0,61.10064764292961,0.06639095799982897 +7,293.9906938530544,0.0,0.0,62.37609122240881,0.06777683399923262 +8,287.754807039453,0.0,0.0,61.05302129920833,0.06633920799868065 +9,283.93805875369935,0.0,0.0,60.24322070271687,0.0654592920000141 +10,288.1126656850648,0.0,0.0,61.128948272374934,0.06642170899795019 +11,287.02066784948323,0.0,0.0,60.8972587732476,0.0661699590018543 +12,283.96806213484086,0.0,0.0,60.249586528840595,0.06546620900189737 +13,287.49473153180446,0.0,0.0,60.99784100293028,0.06627924999702373 +14,290.36370810703596,0.0,0.0,61.60655259929476,0.06694066599811777 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000000000..6bd737e5a Binary files /dev/null and b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-9/raw/plots/violins/gpu_violin.png differ diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-9/raw/plots/violins/time_violin.png b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-9/raw/plots/violins/time_violin.png new file mode 100644 index 000000000..542e2885e Binary files /dev/null and b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/mac/phase-9/raw/plots/violins/time_violin.png differ diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/cleaned/mac/mac_report.md new file mode 100644 index 000000000..6f25ee71a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 890 samples (with smell) vs 875 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 473.04 ms | 65.11 ms | +| **Average Power** | 4.361 W | 5.933 W | +| **Total Energy** | 1835.92 J | 337.99 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.86% | 0.00e+00 | +5.782 | large | βœ… | +| `gpu_mj` | +90.68% | 4.91e-137 | +1.397 | large | βœ… | +| `ane_mj` | +85.47% | 8.58e-28 | +0.535 | medium | βœ… | +| `dram_mj` | +86.08% | 0.00e+00 | +28.390 | large | βœ… | +| `time_s` | +86.12% | 0.00e+00 | +111.194 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.9% lower energy (Cohen’s d = +5.782, large) +- **`gpu_mj`**: 90.7% lower energy (Cohen’s d = +1.397, large) +- **`ane_mj`**: 85.5% lower energy (Cohen’s d = +0.535, medium) +- **`dram_mj`**: 86.1% lower energy (Cohen’s d = +28.390, large) +- **`time_s`**: 86.1% lower time (Cohen’s d = +111.194, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..5699ab346 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/cleaned/mac/with_smell.csv @@ -0,0 +1,891 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,2169.9882565573043,3.135010483643474,76.1312545870052,490.4146399729018,0.4837353750008333 +1,2199.517811199534,3.177672218379119,77.16726113474346,497.08828249823256,0.4903181249974295 +2,2210.1428524823764,3.19302235481889,77.54002707965452,499.4895285785634,0.4926866669993615 +3,2199.071091883712,3.1770268371268866,77.1515885605445,496.98732448992274,0.4902185419996385 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+28,287.6597979585023,0.0,0.0,61.14813981884897,0.0663680840007145 diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/raw/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/raw/mac/mac_report.md new file mode 100644 index 000000000..31742836b --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 471.54 ms | 64.80 ms | +| **Average Power** | 4.373 W | 5.938 W | +| **Total Energy** | 1856.01 J | 346.36 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.93% | 0.00e+00 | +5.793 | large | βœ… | +| `gpu_mj` | +90.91% | 1.99e-139 | +1.403 | large | βœ… | +| `ane_mj` | +85.73% | 4.05e-28 | +0.536 | medium | βœ… | +| `dram_mj` | +86.12% | 0.00e+00 | +28.295 | large | βœ… | +| `time_s` | +86.15% | 0.00e+00 | +109.558 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.9% lower energy (Cohen’s d = +5.793, large) +- **`gpu_mj`**: 90.9% lower energy (Cohen’s d = +1.403, large) +- **`ane_mj`**: 85.7% lower energy (Cohen’s d = +0.536, medium) +- **`dram_mj`**: 86.1% lower energy (Cohen’s d = +28.295, large) +- **`time_s`**: 86.1% lower time (Cohen’s d = +109.558, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/raw/mac/with_smell.csv new file mode 100644 index 000000000..f641e9c6a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n10/results/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,2169.9882565573043,3.135010483643474,76.1312545870052,490.4146399729018,0.4837353750008333 +1,2199.517811199534,3.177672218379119,77.16726113474346,497.08828249823256,0.4903181249974295 +2,2210.1428524823764,3.19302235481889,77.54002707965452,499.4895285785634,0.4926866669993615 +3,2199.071091883712,3.1770268371268866,77.1515885605445,496.98732448992274,0.4902185419996385 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Binary files /dev/null and b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/mac/phase-9/raw/plots/violins/time_violin.png differ diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/cleaned/mac/mac_report.md new file mode 100644 index 000000000..5290d4029 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/cleaned/mac/mac_report.md @@ -0,0 +1,41 @@ +## Energy Report β€” `mac` (cleaned) + +> 887 samples (with smell) vs 872 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 505.49 ms | 77.68 ms | +| **Average Power** | 4.241 W | 3.619 W | +| **Total Energy** | 1901.70 J | 245.10 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.32% | 0.00e+00 | +84.860 | large | βœ… | +| `gpu_mj` | +81.92% | 0.00e+00 | +2.516 | large | βœ… | +| `dram_mj` | +87.77% | 0.00e+00 | +177.673 | large | βœ… | +| `time_s` | +84.39% | 0.00e+00 | +250.581 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.3% lower energy (Cohen’s d = +84.860, large) +- **`gpu_mj`**: 81.9% lower energy (Cohen’s d = +2.516, large) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +177.673, large) +- **`time_s`**: 84.4% lower time (Cohen’s d = +250.581, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..efc857317 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/cleaned/mac/with_smell.csv @@ -0,0 +1,888 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1682.686158652221,2.5057871078333047,0.0,471.7227756733124,0.5124309160019038 +1,1683.8252067083465,2.507483331409856,0.0,472.0420954156768,0.5127777919988148 +2,1676.8147099354564,2.4970435875855617,0.0,470.0767788416073,0.5106428750004852 +3,1694.2890935349883,2.5230657218474333,0.0,474.9755323568521,0.5159643750012037 +4,1681.3039830173152,2.5037288286533763,0.0,471.3352977567602,0.5120100000021921 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mode 100644 index 000000000..cfd12a070 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/raw/mac/mac_report.md @@ -0,0 +1,41 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 504.53 ms | 78.18 ms | +| **Average Power** | 4.258 W | 3.585 W | +| **Total Energy** | 1933.48 J | 252.27 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.36% | 0.00e+00 | +81.417 | large | βœ… | +| `gpu_mj` | +81.96% | 0.00e+00 | +2.519 | large | βœ… | +| `dram_mj` | +87.79% | 0.00e+00 | +170.568 | large | βœ… | +| `time_s` | +84.43% | 0.00e+00 | +221.489 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.4% lower energy (Cohen’s d = +81.417, large) +- **`gpu_mj`**: 82.0% lower energy (Cohen’s d = +2.519, large) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +170.568, large) +- **`time_s`**: 84.4% lower time (Cohen’s d = +221.489, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/raw/mac/with_smell.csv new file mode 100644 index 000000000..c0f6243ae --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n100/results/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1682.686158652221,2.5057871078333047,0.0,471.7227756733124,0.5124309160019038 +1,1683.8252067083465,2.507483331409856,0.0,472.0420954156768,0.5127777919988148 +2,1676.8147099354564,2.4970435875855617,0.0,470.0767788416073,0.5106428750004852 +3,1694.2890935349883,2.5230657218474333,0.0,474.9755323568521,0.5159643750012037 +4,1681.3039830173152,2.5037288286533763,0.0,471.3352977567602,0.5120100000021921 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+++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/mac/phase-3/cleaned/csv/history_with_smell.csv @@ -0,0 +1,30 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,4277.054545303584,1.822999248224933,0.0,670.4328326153767,0.6582882920010888 +1,4324.794879234502,1.843347502366708,0.0,677.9161805522074,0.6656360829983896 +2,4296.3093777503855,1.8312061917425835,0.0,673.4510480068589,0.6612518339970848 +3,4304.37326009285,1.8346432420051824,0.0,674.7150701097968,0.6624929589997919 +4,4281.318895847793,1.8248168326756147,0.0,671.1012740823575,0.6589446250000037 +5,4313.502682660626,1.8385344550588152,0.0,676.1461167722665,0.663898082999367 +6,4294.138215844873,1.8302807823330003,0.0,673.1107160772655,0.66091766700265 +7,4303.817200621714,1.8344062340392768,0.0,674.6279071984083,0.6624073749990202 +8,4302.398366656738,1.8338014876597044,0.0,674.4055034707789,0.6621889999987616 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100644 index 000000000..ad6d0b101 Binary files /dev/null and b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/mac/phase-9/raw/plots/violins/time_violin.png differ diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/cleaned/mac/mac_report.md new file mode 100644 index 000000000..ad3eea23d --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 883 samples (with smell) vs 876 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 667.61 ms | 125.92 ms | +| **Average Power** | 7.811 W | 4.028 W | +| **Total Energy** | 4604.63 J | 444.29 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +90.58% | 0.00e+00 | +12.693 | large | βœ… | +| `gpu_mj` | +85.03% | 2.06e-296 | +2.501 | large | βœ… | +| `ane_mj` | +81.14% | 2.51e-31 | +0.575 | medium | βœ… | +| `dram_mj` | +87.28% | 0.00e+00 | +24.469 | large | βœ… | +| `time_s` | +81.01% | 0.00e+00 | +98.450 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 90.6% lower energy (Cohen’s d = +12.693, large) +- **`gpu_mj`**: 85.0% lower energy (Cohen’s d = +2.501, large) +- **`ane_mj`**: 81.1% lower energy (Cohen’s d = +0.575, medium) +- **`dram_mj`**: 87.3% lower energy (Cohen’s d = +24.469, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +98.450, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..1909cc898 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/cleaned/mac/with_smell.csv @@ -0,0 +1,884 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,5283.835514660083,4.506199071865615,114.09028464916054,751.4003504204952,0.6848855419993924 +1,5273.426802443005,4.4973222381334175,113.86553636992608,749.9201548342323,0.6835363749996759 +2,5277.839087250254,4.501085154985781,113.9608078499363,750.5476142509995,0.6841082909995748 +3,5291.932320688689,4.513104248930637,114.26511350255495,752.5517759087228,0.6859350420018018 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file mode 100644 index 000000000..3003b419e --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 665.57 ms | 123.63 ms | +| **Average Power** | 7.826 W | 4.087 W | +| **Total Energy** | 4687.65 J | 454.69 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +90.61% | 0.00e+00 | +12.775 | large | βœ… | +| `gpu_mj` | +85.05% | 1.48e-302 | +2.504 | large | βœ… | +| `ane_mj` | +81.23% | 1.02e-31 | +0.573 | medium | βœ… | +| `dram_mj` | +87.31% | 0.00e+00 | +24.607 | large | βœ… | +| `time_s` | +81.02% | 0.00e+00 | +97.082 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 90.6% lower energy (Cohen’s d = +12.775, large) +- **`gpu_mj`**: 85.0% lower energy (Cohen’s d = +2.504, large) +- **`ane_mj`**: 81.2% lower energy (Cohen’s d = +0.573, medium) +- **`dram_mj`**: 87.3% lower energy (Cohen’s d = +24.607, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +97.082, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/raw/mac/with_smell.csv new file mode 100644 index 000000000..630c9e720 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n1000/results/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,5283.835514660083,4.506199071865615,114.09028464916054,751.4003504204952,0.6848855419993924 +1,5273.426802443005,4.4973222381334175,113.86553636992608,749.9201548342323,0.6835363749996759 +2,5277.839087250254,4.501085154985781,113.9608078499363,750.5476142509995,0.6841082909995748 +3,5291.932320688689,4.513104248930637,114.26511350255495,752.5517759087228,0.6859350420018018 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/dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 898 samples (with smell) vs 881 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 490.02 ms | 75.55 ms | +| **Average Power** | 4.325 W | 4.612 W | +| **Total Energy** | 1903.20 J | 307.00 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +83.33% | 0.00e+00 | +6.279 | large | βœ… | +| `gpu_mj` | +83.08% | 8.41e-224 | +1.932 | large | βœ… | +| `ane_mj` | +86.82% | 6.94e-29 | +0.545 | medium | βœ… | +| `dram_mj` | +85.80% | 0.00e+00 | +28.191 | large | βœ… | +| `time_s` | +84.45% | 0.00e+00 | +112.566 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 83.3% lower energy (Cohen’s d = +6.279, large) +- **`gpu_mj`**: 83.1% lower energy (Cohen’s d = +1.932, large) +- **`ane_mj`**: 86.8% lower energy (Cohen’s d = +0.545, medium) +- **`dram_mj`**: 85.8% lower energy (Cohen’s d = +28.191, large) +- **`time_s`**: 84.5% lower time (Cohen’s d = +112.566, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..e5077d203 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/cleaned/mac/with_smell.csv @@ -0,0 +1,899 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1562.7525821795537,1.3534612562180337,0.0,463.1808508779323,0.4898252499988302 +1,1588.3625529242652,1.3756414167700648,0.0,470.77133460245807,0.4978523749996384 +2,1564.88152978786,1.355305084945126,0.0,463.8118450454893,0.4904925419978099 +3,1585.4859825717,1.3731500905247085,0.0,469.9187541500533,0.4969507500027248 +4,1587.2227722401722,1.3746542809853626,0.0,470.4335174757469,0.4974951250005688 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a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/raw/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/raw/mac/mac_report.md new file mode 100644 index 000000000..abd4a7433 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 489.50 ms | 75.33 ms | +| **Average Power** | 4.329 W | 4.613 W | +| **Total Energy** | 1907.29 J | 312.72 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +83.37% | 0.00e+00 | +6.288 | large | βœ… | +| `gpu_mj` | +83.10% | 1.39e-224 | +1.934 | large | βœ… | +| `ane_mj` | +86.98% | 5.53e-29 | +0.545 | medium | βœ… | +| `dram_mj` | +85.83% | 0.00e+00 | +28.185 | large | βœ… | +| `time_s` | +84.47% | 0.00e+00 | +111.097 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 83.4% lower energy (Cohen’s d = +6.288, large) +- **`gpu_mj`**: 83.1% lower energy (Cohen’s d = +1.934, large) +- **`ane_mj`**: 87.0% lower energy (Cohen’s d = +0.545, medium) +- **`dram_mj`**: 85.8% lower energy (Cohen’s d = +28.185, large) +- **`time_s`**: 84.5% lower time (Cohen’s d = +111.097, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/raw/mac/with_smell.csv new file mode 100644 index 000000000..7aa2a9216 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n30/results/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1562.7525821795537,1.3534612562180337,0.0,463.1808508779323,0.4898252499988302 +1,1588.3625529242652,1.3756414167700648,0.0,470.77133460245807,0.4978523749996384 +2,1564.88152978786,1.355305084945126,0.0,463.8118450454893,0.4904925419978099 +3,1585.4859825717,1.3731500905247085,0.0,469.9187541500533,0.4969507500027248 +4,1587.2227722401722,1.3746542809853626,0.0,470.4335174757469,0.4974951250005688 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n50/results/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 891 samples (with smell) vs 890 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 500.03 ms | 61.56 ms | +| **Average Power** | 5.624 W | 6.458 W | +| **Total Energy** | 2505.74 J | 353.85 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +85.38% | 0.00e+00 | +7.249 | large | βœ… | +| `gpu_mj` | +87.81% | 0.00e+00 | +3.995 | large | βœ… | +| `ane_mj` | +87.93% | 1.45e-279 | +2.494 | large | βœ… | +| `dram_mj` | +87.72% | 0.00e+00 | +31.230 | large | βœ… | +| `time_s` | +87.70% | 0.00e+00 | +115.051 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 85.4% lower energy (Cohen’s d = +7.249, large) +- **`gpu_mj`**: 87.8% lower energy (Cohen’s d = +3.995, large) +- **`ane_mj`**: 87.9% lower energy (Cohen’s d = +2.494, large) +- **`dram_mj`**: 87.7% lower energy (Cohen’s d = +31.230, large) +- **`time_s`**: 87.7% lower time (Cohen’s d = +115.051, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. 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`Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 499.59 ms | 61.33 ms | +| **Average Power** | 5.623 W | 6.479 W | +| **Total Energy** | 2528.48 J | 357.61 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +85.36% | 0.00e+00 | +7.194 | large | βœ… | +| `gpu_mj` | +87.85% | 0.00e+00 | +3.974 | large | βœ… | +| `ane_mj` | +87.88% | 7.64e-279 | +2.465 | large | βœ… | +| `dram_mj` | +87.74% | 0.00e+00 | +31.039 | large | βœ… | +| `time_s` | +87.71% | 0.00e+00 | +113.485 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 85.4% lower energy (Cohen’s d = +7.194, large) +- **`gpu_mj`**: 87.8% lower energy (Cohen’s d = +3.974, large) +- **`ane_mj`**: 87.9% lower energy (Cohen’s d = +2.465, large) +- **`dram_mj`**: 87.7% lower energy (Cohen’s d = +31.039, large) +- **`time_s`**: 87.7% lower time (Cohen’s d = +113.485, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n50/results/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n50/results/raw/mac/with_smell.csv new file mode 100644 index 000000000..e21f0f73b --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n50/results/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,2434.6814691630375,4.042822843304115,79.82069233597959,501.04273849742566,0.5069931250000081 +1,2447.3972914027354,4.06393764508544,80.23757879429023,503.6595778983575,0.5096410420010216 +2,2449.666330031311,4.067705415660825,80.31196890920423,504.1265323409069,0.5101135419972707 +3,2430.7570695872123,4.036306322579597,79.69203144332776,500.235120771931,0.5061759160016663 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differ diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n500/results/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n500/results/cleaned/mac/mac_report.md new file mode 100644 index 000000000..20c6d52c2 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n500/results/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 875 samples (with smell) vs 877 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 613.25 ms | 108.37 ms | +| **Average Power** | 6.227 W | 4.551 W | +| **Total Energy** | 3341.35 J | 432.52 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +87.24% | 0.00e+00 | +8.245 | large | βœ… | +| `gpu_mj` | +84.73% | 1.53e-265 | +2.269 | large | βœ… | +| `ane_mj` | +81.57% | 7.15e-43 | +0.691 | medium | βœ… | +| `dram_mj` | +86.54% | 0.00e+00 | +19.272 | large | βœ… | +| `time_s` | +82.21% | 0.00e+00 | +92.465 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 87.2% lower energy (Cohen’s d = +8.245, large) +- **`gpu_mj`**: 84.7% lower energy (Cohen’s d = +2.269, large) +- **`ane_mj`**: 81.6% lower energy (Cohen’s d = +0.691, medium) +- **`dram_mj`**: 86.5% lower energy (Cohen’s d = +19.272, large) +- **`time_s`**: 82.2% lower time (Cohen’s d = +92.465, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. 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+|---|---:|---:| +| **Execution Time** | 611.35 ms | 106.11 ms | +| **Average Power** | 6.242 W | 4.652 W | +| **Total Energy** | 3434.22 J | 444.25 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +87.22% | 0.00e+00 | +8.263 | large | βœ… | +| `gpu_mj` | +84.75% | 3.81e-273 | +2.269 | large | βœ… | +| `ane_mj` | +81.59% | 2.97e-43 | +0.684 | medium | βœ… | +| `dram_mj` | +86.52% | 0.00e+00 | +19.310 | large | βœ… | +| `time_s` | +82.21% | 0.00e+00 | +92.123 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 87.2% lower energy (Cohen’s d = +8.263, large) +- **`gpu_mj`**: 84.7% lower energy (Cohen’s d = +2.269, large) +- **`ane_mj`**: 81.6% lower energy (Cohen’s d = +0.684, medium) +- **`dram_mj`**: 86.5% lower energy (Cohen’s d = +19.310, large) +- **`time_s`**: 82.2% lower time (Cohen’s d = +92.123, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n500/results/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n500/results/raw/mac/with_smell.csv new file mode 100644 index 000000000..164636486 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/out-tmp/fetchtype-n500/results/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,3901.919386580033,5.181302556234977,102.39715244148996,658.0918515976913,0.6241167499974836 +1,3905.882313647163,5.186564870011554,102.5011506041386,658.7602329128778,0.6247506250001607 +2,3910.238854451517,5.192349857800788,102.61547827948608,659.4950005286206,0.6254474589986785 +3,3911.202429509975,5.193629375243073,102.64076515304096,659.6575156478605,0.6256015840008331 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+28,355.80325111504544,0.0,0.0,73.98925400286308,0.1092134579994308 +29,354.60680161162924,0.0,0.0,73.7404524364568,0.108846209001058 diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/cleaned/mac/mac_report.md new file mode 100644 index 000000000..6f25ee71a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 890 samples (with smell) vs 875 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 473.04 ms | 65.11 ms | +| **Average Power** | 4.361 W | 5.933 W | +| **Total Energy** | 1835.92 J | 337.99 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.86% | 0.00e+00 | +5.782 | large | βœ… | +| `gpu_mj` | +90.68% | 4.91e-137 | +1.397 | large | βœ… | +| `ane_mj` | +85.47% | 8.58e-28 | +0.535 | medium | βœ… | +| `dram_mj` | +86.08% | 0.00e+00 | +28.390 | large | βœ… | +| `time_s` | +86.12% | 0.00e+00 | +111.194 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.9% lower energy (Cohen’s d = +5.782, large) +- **`gpu_mj`**: 90.7% lower energy (Cohen’s d = +1.397, large) +- **`ane_mj`**: 85.5% lower energy (Cohen’s d = +0.535, medium) +- **`dram_mj`**: 86.1% lower energy (Cohen’s d = +28.390, large) +- **`time_s`**: 86.1% lower time (Cohen’s d = +111.194, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..5699ab346 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/cleaned/mac/with_smell.csv @@ -0,0 +1,891 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,2169.9882565573043,3.135010483643474,76.1312545870052,490.4146399729018,0.4837353750008333 +1,2199.517811199534,3.177672218379119,77.16726113474346,497.08828249823256,0.4903181249974295 +2,2210.1428524823764,3.19302235481889,77.54002707965452,499.4895285785634,0.4926866669993615 +3,2199.071091883712,3.1770268371268866,77.1515885605445,496.98732448992274,0.4902185419996385 +4,2209.0770745397144,3.1914826114523605,77.5026356275852,499.24866409256344,0.4924490829980641 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+28,287.6597979585023,0.0,0.0,61.14813981884897,0.0663680840007145 diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/raw/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/raw/mac/mac_report.md new file mode 100644 index 000000000..31742836b --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 471.54 ms | 64.80 ms | +| **Average Power** | 4.373 W | 5.938 W | +| **Total Energy** | 1856.01 J | 346.36 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +79.93% | 0.00e+00 | +5.793 | large | βœ… | +| `gpu_mj` | +90.91% | 1.99e-139 | +1.403 | large | βœ… | +| `ane_mj` | +85.73% | 4.05e-28 | +0.536 | medium | βœ… | +| `dram_mj` | +86.12% | 0.00e+00 | +28.295 | large | βœ… | +| `time_s` | +86.15% | 0.00e+00 | +109.558 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 79.9% lower energy (Cohen’s d = +5.793, large) +- **`gpu_mj`**: 90.9% lower energy (Cohen’s d = +1.403, large) +- **`ane_mj`**: 85.7% lower energy (Cohen’s d = +0.536, medium) +- **`dram_mj`**: 86.1% lower energy (Cohen’s d = +28.295, large) +- **`time_s`**: 86.1% lower time (Cohen’s d = +109.558, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/raw/mac/with_smell.csv new file mode 100644 index 000000000..f641e9c6a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n10/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,2169.9882565573043,3.135010483643474,76.1312545870052,490.4146399729018,0.4837353750008333 +1,2199.517811199534,3.177672218379119,77.16726113474346,497.08828249823256,0.4903181249974295 +2,2210.1428524823764,3.19302235481889,77.54002707965452,499.4895285785634,0.4926866669993615 +3,2199.071091883712,3.1770268371268866,77.1515885605445,496.98732448992274,0.4902185419996385 +4,2209.0770745397144,3.1914826114523605,77.5026356275852,499.24866409256344,0.4924490829980641 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n100/cleaned/mac/mac_report.md new file mode 100644 index 000000000..5290d4029 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n100/cleaned/mac/mac_report.md @@ -0,0 +1,41 @@ +## Energy Report β€” `mac` (cleaned) + +> 887 samples (with smell) vs 872 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 505.49 ms | 77.68 ms | +| **Average Power** | 4.241 W | 3.619 W | +| **Total Energy** | 1901.70 J | 245.10 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.32% | 0.00e+00 | +84.860 | large | βœ… | +| `gpu_mj` | +81.92% | 0.00e+00 | +2.516 | large | βœ… | +| `dram_mj` | +87.77% | 0.00e+00 | +177.673 | large | βœ… | +| `time_s` | +84.39% | 0.00e+00 | +250.581 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.3% lower energy (Cohen’s d = +84.860, large) +- **`gpu_mj`**: 81.9% lower energy (Cohen’s d = +2.516, large) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +177.673, large) +- **`time_s`**: 84.4% lower time (Cohen’s d = +250.581, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n100/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n100/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..efc857317 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n100/cleaned/mac/with_smell.csv @@ -0,0 +1,888 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1682.686158652221,2.5057871078333047,0.0,471.7227756733124,0.5124309160019038 +1,1683.8252067083465,2.507483331409856,0.0,472.0420954156768,0.5127777919988148 +2,1676.8147099354564,2.4970435875855617,0.0,470.0767788416073,0.5106428750004852 +3,1694.2890935349883,2.5230657218474333,0.0,474.9755323568521,0.5159643750012037 +4,1681.3039830173152,2.5037288286533763,0.0,471.3352977567602,0.5120100000021921 +5,1685.0352589289462,2.5092852914715067,0.0,472.38132040381606,0.5131462909994298 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000000000..cfd12a070 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n100/raw/mac/mac_report.md @@ -0,0 +1,41 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 504.53 ms | 78.18 ms | +| **Average Power** | 4.258 W | 3.585 W | +| **Total Energy** | 1933.48 J | 252.27 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +86.36% | 0.00e+00 | +81.417 | large | βœ… | +| `gpu_mj` | +81.96% | 0.00e+00 | +2.519 | large | βœ… | +| `dram_mj` | +87.79% | 0.00e+00 | +170.568 | large | βœ… | +| `time_s` | +84.43% | 0.00e+00 | +221.489 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 86.4% lower energy (Cohen’s d = +81.417, large) +- **`gpu_mj`**: 82.0% lower energy (Cohen’s d = +2.519, large) +- **`dram_mj`**: 87.8% lower energy (Cohen’s d = +170.568, large) +- **`time_s`**: 84.4% lower time (Cohen’s d = +221.489, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n100/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n100/raw/mac/with_smell.csv new file mode 100644 index 000000000..c0f6243ae --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n100/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1682.686158652221,2.5057871078333047,0.0,471.7227756733124,0.5124309160019038 +1,1683.8252067083465,2.507483331409856,0.0,472.0420954156768,0.5127777919988148 +2,1676.8147099354564,2.4970435875855617,0.0,470.0767788416073,0.5106428750004852 +3,1694.2890935349883,2.5230657218474333,0.0,474.9755323568521,0.5159643750012037 +4,1681.3039830173152,2.5037288286533763,0.0,471.3352977567602,0.5120100000021921 +5,1685.0352589289462,2.5092852914715067,0.0,472.38132040381606,0.5131462909994298 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a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/cleaned/mac/mac_report.md new file mode 100644 index 000000000..ad3eea23d --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 883 samples (with smell) vs 876 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 667.61 ms | 125.92 ms | +| **Average Power** | 7.811 W | 4.028 W | +| **Total Energy** | 4604.63 J | 444.29 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +90.58% | 0.00e+00 | +12.693 | large | βœ… | +| `gpu_mj` | +85.03% | 2.06e-296 | +2.501 | large | βœ… | +| `ane_mj` | +81.14% | 2.51e-31 | +0.575 | medium | βœ… | +| `dram_mj` | +87.28% | 0.00e+00 | +24.469 | large | βœ… | +| `time_s` | +81.01% | 0.00e+00 | +98.450 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 90.6% lower energy (Cohen’s d = +12.693, large) +- **`gpu_mj`**: 85.0% lower energy (Cohen’s d = +2.501, large) +- **`ane_mj`**: 81.1% lower energy (Cohen’s d = +0.575, medium) +- **`dram_mj`**: 87.3% lower energy (Cohen’s d = +24.469, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +98.450, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..1909cc898 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/cleaned/mac/with_smell.csv @@ -0,0 +1,884 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,5283.835514660083,4.506199071865615,114.09028464916054,751.4003504204952,0.6848855419993924 +1,5273.426802443005,4.4973222381334175,113.86553636992608,749.9201548342323,0.6835363749996759 +2,5277.839087250254,4.501085154985781,113.9608078499363,750.5476142509995,0.6841082909995748 +3,5291.932320688689,4.513104248930637,114.26511350255495,752.5517759087228,0.6859350420018018 +4,5334.267930950258,4.54920921229262,115.17923768604574,758.5722116882902,0.6914225420005096 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000000000..3003b419e --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 665.57 ms | 123.63 ms | +| **Average Power** | 7.826 W | 4.087 W | +| **Total Energy** | 4687.65 J | 454.69 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +90.61% | 0.00e+00 | +12.775 | large | βœ… | +| `gpu_mj` | +85.05% | 1.48e-302 | +2.504 | large | βœ… | +| `ane_mj` | +81.23% | 1.02e-31 | +0.573 | medium | βœ… | +| `dram_mj` | +87.31% | 0.00e+00 | +24.607 | large | βœ… | +| `time_s` | +81.02% | 0.00e+00 | +97.082 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 90.6% lower energy (Cohen’s d = +12.775, large) +- **`gpu_mj`**: 85.0% lower energy (Cohen’s d = +2.504, large) +- **`ane_mj`**: 81.2% lower energy (Cohen’s d = +0.573, medium) +- **`dram_mj`**: 87.3% lower energy (Cohen’s d = +24.607, large) +- **`time_s`**: 81.0% lower time (Cohen’s d = +97.082, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/raw/mac/with_smell.csv new file mode 100644 index 000000000..630c9e720 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n1000/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,5283.835514660083,4.506199071865615,114.09028464916054,751.4003504204952,0.6848855419993924 +1,5273.426802443005,4.4973222381334175,113.86553636992608,749.9201548342323,0.6835363749996759 +2,5277.839087250254,4.501085154985781,113.9608078499363,750.5476142509995,0.6841082909995748 +3,5291.932320688689,4.513104248930637,114.26511350255495,752.5517759087228,0.6859350420018018 +4,5334.267930950258,4.54920921229262,115.17923768604574,758.5722116882902,0.6914225420005096 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-0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 898 samples (with smell) vs 881 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 490.02 ms | 75.55 ms | +| **Average Power** | 4.325 W | 4.612 W | +| **Total Energy** | 1903.20 J | 307.00 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +83.33% | 0.00e+00 | +6.279 | large | βœ… | +| `gpu_mj` | +83.08% | 8.41e-224 | +1.932 | large | βœ… | +| `ane_mj` | +86.82% | 6.94e-29 | +0.545 | medium | βœ… | +| `dram_mj` | +85.80% | 0.00e+00 | +28.191 | large | βœ… | +| `time_s` | +84.45% | 0.00e+00 | +112.566 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 83.3% lower energy (Cohen’s d = +6.279, large) +- **`gpu_mj`**: 83.1% lower energy (Cohen’s d = +1.932, large) +- **`ane_mj`**: 86.8% lower energy (Cohen’s d = +0.545, medium) +- **`dram_mj`**: 85.8% lower energy (Cohen’s d = +28.191, large) +- **`time_s`**: 84.5% lower time (Cohen’s d = +112.566, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n30/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n30/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..e5077d203 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n30/cleaned/mac/with_smell.csv @@ -0,0 +1,899 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1562.7525821795537,1.3534612562180337,0.0,463.1808508779323,0.4898252499988302 +1,1588.3625529242652,1.3756414167700648,0.0,470.77133460245807,0.4978523749996384 +2,1564.88152978786,1.355305084945126,0.0,463.8118450454893,0.4904925419978099 +3,1585.4859825717,1.3731500905247085,0.0,469.9187541500533,0.4969507500027248 +4,1587.2227722401722,1.3746542809853626,0.0,470.4335174757469,0.4974951250005688 +5,1587.7919989684403,1.3751472741382773,0.0,470.6022293520529,0.4976735419986653 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b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n30/raw/mac/mac_report.md new file mode 100644 index 000000000..abd4a7433 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n30/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 489.50 ms | 75.33 ms | +| **Average Power** | 4.329 W | 4.613 W | +| **Total Energy** | 1907.29 J | 312.72 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +83.37% | 0.00e+00 | +6.288 | large | βœ… | +| `gpu_mj` | +83.10% | 1.39e-224 | +1.934 | large | βœ… | +| `ane_mj` | +86.98% | 5.53e-29 | +0.545 | medium | βœ… | +| `dram_mj` | +85.83% | 0.00e+00 | +28.185 | large | βœ… | +| `time_s` | +84.47% | 0.00e+00 | +111.097 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 83.4% lower energy (Cohen’s d = +6.288, large) +- **`gpu_mj`**: 83.1% lower energy (Cohen’s d = +1.934, large) +- **`ane_mj`**: 87.0% lower energy (Cohen’s d = +0.545, medium) +- **`dram_mj`**: 85.8% lower energy (Cohen’s d = +28.185, large) +- **`time_s`**: 84.5% lower time (Cohen’s d = +111.097, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n30/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n30/raw/mac/with_smell.csv new file mode 100644 index 000000000..7aa2a9216 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n30/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,1562.7525821795537,1.3534612562180337,0.0,463.1808508779323,0.4898252499988302 +1,1588.3625529242652,1.3756414167700648,0.0,470.77133460245807,0.4978523749996384 +2,1564.88152978786,1.355305084945126,0.0,463.8118450454893,0.4904925419978099 +3,1585.4859825717,1.3731500905247085,0.0,469.9187541500533,0.4969507500027248 +4,1587.2227722401722,1.3746542809853626,0.0,470.4335174757469,0.4974951250005688 +5,1587.7919989684403,1.3751472741382773,0.0,470.6022293520529,0.4976735419986653 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+29,258.87526808998655,0.0,0.0,64.12939245945022,0.0773215000008349 diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n50/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n50/cleaned/mac/mac_report.md new file mode 100644 index 000000000..9ad8b0ac5 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n50/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 891 samples (with smell) vs 890 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 500.03 ms | 61.56 ms | +| **Average Power** | 5.624 W | 6.458 W | +| **Total Energy** | 2505.74 J | 353.85 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +85.38% | 0.00e+00 | +7.249 | large | βœ… | +| `gpu_mj` | +87.81% | 0.00e+00 | +3.995 | large | βœ… | +| `ane_mj` | +87.93% | 1.45e-279 | +2.494 | large | βœ… | +| `dram_mj` | +87.72% | 0.00e+00 | +31.230 | large | βœ… | +| `time_s` | +87.70% | 0.00e+00 | +115.051 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 85.4% lower energy (Cohen’s d = +7.249, large) +- **`gpu_mj`**: 87.8% lower energy (Cohen’s d = +3.995, large) +- **`ane_mj`**: 87.9% lower energy (Cohen’s d = +2.494, large) +- **`dram_mj`**: 87.7% lower energy (Cohen’s d = +31.230, large) +- **`time_s`**: 87.7% lower time (Cohen’s d = +115.051, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n50/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n50/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..9f8557a3c --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n50/cleaned/mac/with_smell.csv @@ -0,0 +1,892 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,2434.6814691630375,4.042822843304115,79.82069233597959,501.04273849742566,0.5069931250000081 +1,2447.3972914027354,4.06393764508544,80.23757879429023,503.6595778983575,0.5096410420010216 +2,2449.666330031311,4.067705415660825,80.31196890920423,504.1265323409069,0.5101135419972707 +3,2430.7570695872123,4.036306322579597,79.69203144332776,500.235120771931,0.5061759160016663 +4,2423.186010359365,4.023734472182728,79.44381532268211,498.6770425194395,0.5045993340027053 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smell | Without smell | +|---|---:|---:| +| **Execution Time** | 499.59 ms | 61.33 ms | +| **Average Power** | 5.623 W | 6.479 W | +| **Total Energy** | 2528.48 J | 357.61 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +85.36% | 0.00e+00 | +7.194 | large | βœ… | +| `gpu_mj` | +87.85% | 0.00e+00 | +3.974 | large | βœ… | +| `ane_mj` | +87.88% | 7.64e-279 | +2.465 | large | βœ… | +| `dram_mj` | +87.74% | 0.00e+00 | +31.039 | large | βœ… | +| `time_s` | +87.71% | 0.00e+00 | +113.485 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 85.4% lower energy (Cohen’s d = +7.194, large) +- **`gpu_mj`**: 87.8% lower energy (Cohen’s d = +3.974, large) +- **`ane_mj`**: 87.9% lower energy (Cohen’s d = +2.465, large) +- **`dram_mj`**: 87.7% lower energy (Cohen’s d = +31.039, large) +- **`time_s`**: 87.7% lower time (Cohen’s d = +113.485, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n50/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n50/raw/mac/with_smell.csv new file mode 100644 index 000000000..e21f0f73b --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n50/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,2434.6814691630375,4.042822843304115,79.82069233597959,501.04273849742566,0.5069931250000081 +1,2447.3972914027354,4.06393764508544,80.23757879429023,503.6595778983575,0.5096410420010216 +2,2449.666330031311,4.067705415660825,80.31196890920423,504.1265323409069,0.5101135419972707 +3,2430.7570695872123,4.036306322579597,79.69203144332776,500.235120771931,0.5061759160016663 +4,2423.186010359365,4.023734472182728,79.44381532268211,498.6770425194395,0.5045993340027053 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+29,358.61025985762825,1.158786241699236,10.599485916719482,64.58529200058977,0.063656832997367 diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/cleaned/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/cleaned/mac/mac_report.md new file mode 100644 index 000000000..20c6d52c2 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/cleaned/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (cleaned) + +> 875 samples (with smell) vs 877 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 613.25 ms | 108.37 ms | +| **Average Power** | 6.227 W | 4.551 W | +| **Total Energy** | 3341.35 J | 432.52 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +87.24% | 0.00e+00 | +8.245 | large | βœ… | +| `gpu_mj` | +84.73% | 1.53e-265 | +2.269 | large | βœ… | +| `ane_mj` | +81.57% | 7.15e-43 | +0.691 | medium | βœ… | +| `dram_mj` | +86.54% | 0.00e+00 | +19.272 | large | βœ… | +| `time_s` | +82.21% | 0.00e+00 | +92.465 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 87.2% lower energy (Cohen’s d = +8.245, large) +- **`gpu_mj`**: 84.7% lower energy (Cohen’s d = +2.269, large) +- **`ane_mj`**: 81.6% lower energy (Cohen’s d = +0.691, medium) +- **`dram_mj`**: 86.5% lower energy (Cohen’s d = +19.272, large) +- **`time_s`**: 82.2% lower time (Cohen’s d = +92.465, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/cleaned/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/cleaned/mac/with_smell.csv new file mode 100644 index 000000000..db0193cda --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/cleaned/mac/with_smell.csv @@ -0,0 +1,876 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,3901.919386580033,5.181302556234977,102.39715244148996,658.0918515976913,0.6241167499974836 +1,3905.882313647163,5.186564870011554,102.5011506041386,658.7602329128778,0.6247506250001607 +2,3910.238854451517,5.192349857800788,102.61547827948608,659.4950005286206,0.6254474589986785 +3,3911.202429509975,5.193629375243073,102.64076515304096,659.6575156478605,0.6256015840008331 +4,3896.403904938974,5.173978627600272,102.25241095443356,657.1616187645627,0.623234542003047 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+27,338.47464045384555,0.0,0.0,70.38577097759506,0.1038944579995586 +28,355.80325111504544,0.0,0.0,73.98925400286308,0.1092134579994308 +29,354.60680161162924,0.0,0.0,73.7404524364568,0.108846209001058 diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/raw/mac/mac_report.md b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/raw/mac/mac_report.md new file mode 100644 index 000000000..f567e5107 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/raw/mac/mac_report.md @@ -0,0 +1,43 @@ +## Energy Report β€” `mac` (raw) + +> 900 samples (with smell) vs 900 samples (without smell) β€” α = 0.05 + +### Instance Info + +* **hostname**: `M-NGY9VPYVMH` +* **model**: `MacBookPro18,1` +* **os**: `Darwin 25.5.0` +* **machine**: `arm64` +* **chip**: `Apple M1 Pro` + +### Global Consumption + +| | With smell | Without smell | +|---|---:|---:| +| **Execution Time** | 611.35 ms | 106.11 ms | +| **Average Power** | 6.242 W | 4.652 W | +| **Total Energy** | 3434.22 J | 444.25 J | + +> The total energy is the sum of measurements across all iterations, converted to joules (J). If you ran the `./run_experiment.sh` script, this reflects the cumulative energy of all 30 iterations of the process. + +### Statistical Analysis + +| Metric | Ξ” mean | p-value | Cohen’s d | Effect | Sig. | +|---|---|---|---|---|---| +| `cpu_mj` | +87.22% | 0.00e+00 | +8.263 | large | βœ… | +| `gpu_mj` | +84.75% | 3.81e-273 | +2.269 | large | βœ… | +| `ane_mj` | +81.59% | 2.97e-43 | +0.684 | medium | βœ… | +| `dram_mj` | +86.52% | 0.00e+00 | +19.310 | large | βœ… | +| `time_s` | +82.21% | 0.00e+00 | +92.123 | large | βœ… | + +### Verdict + +Removing the code smell leads to measurable energy differences: + +- **`cpu_mj`**: 87.2% lower energy (Cohen’s d = +8.263, large) +- **`gpu_mj`**: 84.7% lower energy (Cohen’s d = +2.269, large) +- **`ane_mj`**: 81.6% lower energy (Cohen’s d = +0.684, medium) +- **`dram_mj`**: 86.5% lower energy (Cohen’s d = +19.310, large) +- **`time_s`**: 82.2% lower time (Cohen’s d = +92.123, large) + +> Ξ” mean = (mean\_with βˆ’ mean\_without) / mean\_with Γ— 100. Positive β†’ the smell consumes more energy. diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/raw/mac/with_smell.csv b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/raw/mac/with_smell.csv new file mode 100644 index 000000000..164636486 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/campaign2/results/n500/raw/mac/with_smell.csv @@ -0,0 +1,901 @@ +i,cpu_mj,gpu_mj,ane_mj,dram_mj,time_s +0,3901.919386580033,5.181302556234977,102.39715244148996,658.0918515976913,0.6241167499974836 +1,3905.882313647163,5.186564870011554,102.5011506041386,658.7602329128778,0.6247506250001607 +2,3910.238854451517,5.192349857800788,102.61547827948608,659.4950005286206,0.6254474589986785 +3,3911.202429509975,5.193629375243073,102.64076515304096,659.6575156478605,0.6256015840008331 +4,3896.403904938974,5.173978627600272,102.25241095443356,657.1616187645627,0.623234542003047 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+28,355.80325111504544,0.0,0.0,73.98925400286308,0.1092134579994308 +29,354.60680161162924,0.0,0.0,73.7404524364568,0.108846209001058 diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/template/Eager.java.tpl b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/template/Eager.java.tpl new file mode 100644 index 000000000..352ef2480 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/template/Eager.java.tpl @@ -0,0 +1,121 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITH the code smell: the child collection is fetched eagerly. + * + *

Models the client side of a JPA read where a parent entity is loaded together with a + * collection association mapped with {@code FetchType.EAGER}, while the application never + * accesses that collection. + * + *

This file is measured by EnergyTracer, whose Java runner compiles with a bare + * {@code javac } and runs with {@code java -cp }: no third-party + * library is reachable, hence the hand-rolled row source instead of JDBC/Hibernate. + * See README.md for what this does and does not measure. + * + *

The only difference with the compliant variant is the body of {@code loadOrder}. + */ +public class __CLASS__ { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = __CHILD_COUNT__; + + /** Parent reads performed by one JVM run. Calibrated so a run lasts ~300-500 ms, + * so that JVM startup (~100-400 ms, once per iteration) does not drown the signal. */ + private static final int PARENT_READS = __PARENT_READS__; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // EAGER: the provider hydrates the whole collection now, used or not. + Set items = hydrateItems(p); + return new Order(id, reference, items); + } + + private static Set hydrateItems(int p) { + Set items = new HashSet<>(Math.max(16, CHILD_COUNT * 2)); + int base = p * CHILD_COUNT * CHILD_FIELDS; + for (int i = 0; i < CHILD_COUNT; i++) { + int o = base + i * CHILD_FIELDS; + items.add(new OrderItem( + CHILD_WIRE[o], + CHILD_WIRE[o + 1], + CHILD_WIRE[o + 2], + "ITEM-" + CHILD_WIRE[o])); + } + return items; + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/template/Lazy.java.tpl b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/template/Lazy.java.tpl new file mode 100644 index 000000000..bac8e7f7d --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/template/Lazy.java.tpl @@ -0,0 +1,104 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITHOUT the code smell: the child collection is fetched lazily. + * + *

Strictly identical to the noncompliant variant except for the body of + * {@code loadOrder}: no call to {@code hydrateItems}. The empty {@code HashSet} + * stands for the lazy collection wrapper a real provider allocates, so the + * comparison stays conservative (the compliant variant is not free). + * + *

See README.md for what this does and does not measure. + */ +public class __CLASS__ { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = __CHILD_COUNT__; + + /** Parent reads performed by one JVM run. Must be identical to the eager variant. */ + private static final int PARENT_READS = __PARENT_READS__; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. + * Allocated in both variants so the two share the same memory footprint. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // LAZY: only the collection wrapper is allocated. Rows are never fetched + // because the application never touches the collection. + Set items = new HashSet<>(); + return new Order(id, reference, items); + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN10.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN10.java new file mode 100644 index 000000000..f602a9b8a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN10.java @@ -0,0 +1,121 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITH the code smell: the child collection is fetched eagerly. + * + *

Models the client side of a JPA read where a parent entity is loaded together with a + * collection association mapped with {@code FetchType.EAGER}, while the application never + * accesses that collection. + * + *

This file is measured by EnergyTracer, whose Java runner compiles with a bare + * {@code javac } and runs with {@code java -cp }: no third-party + * library is reachable, hence the hand-rolled row source instead of JDBC/Hibernate. + * See README.md for what this does and does not measure. + * + *

The only difference with the compliant variant is the body of {@code loadOrder}. + */ +public class FetchTypeEagerN10 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 10; + + /** Parent reads performed by one JVM run. Calibrated so a run lasts ~300-500 ms, + * so that JVM startup (~100-400 ms, once per iteration) does not drown the signal. */ + private static final int PARENT_READS = 500000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // EAGER: the provider hydrates the whole collection now, used or not. + Set items = hydrateItems(p); + return new Order(id, reference, items); + } + + private static Set hydrateItems(int p) { + Set items = new HashSet<>(Math.max(16, CHILD_COUNT * 2)); + int base = p * CHILD_COUNT * CHILD_FIELDS; + for (int i = 0; i < CHILD_COUNT; i++) { + int o = base + i * CHILD_FIELDS; + items.add(new OrderItem( + CHILD_WIRE[o], + CHILD_WIRE[o + 1], + CHILD_WIRE[o + 2], + "ITEM-" + CHILD_WIRE[o])); + } + return items; + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN100.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN100.java new file mode 100644 index 000000000..6e8d80015 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN100.java @@ -0,0 +1,121 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITH the code smell: the child collection is fetched eagerly. + * + *

Models the client side of a JPA read where a parent entity is loaded together with a + * collection association mapped with {@code FetchType.EAGER}, while the application never + * accesses that collection. + * + *

This file is measured by EnergyTracer, whose Java runner compiles with a bare + * {@code javac } and runs with {@code java -cp }: no third-party + * library is reachable, hence the hand-rolled row source instead of JDBC/Hibernate. + * See README.md for what this does and does not measure. + * + *

The only difference with the compliant variant is the body of {@code loadOrder}. + */ +public class FetchTypeEagerN100 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 100; + + /** Parent reads performed by one JVM run. Calibrated so a run lasts ~300-500 ms, + * so that JVM startup (~100-400 ms, once per iteration) does not drown the signal. */ + private static final int PARENT_READS = 50000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // EAGER: the provider hydrates the whole collection now, used or not. + Set items = hydrateItems(p); + return new Order(id, reference, items); + } + + private static Set hydrateItems(int p) { + Set items = new HashSet<>(Math.max(16, CHILD_COUNT * 2)); + int base = p * CHILD_COUNT * CHILD_FIELDS; + for (int i = 0; i < CHILD_COUNT; i++) { + int o = base + i * CHILD_FIELDS; + items.add(new OrderItem( + CHILD_WIRE[o], + CHILD_WIRE[o + 1], + CHILD_WIRE[o + 2], + "ITEM-" + CHILD_WIRE[o])); + } + return items; + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN1000.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN1000.java new file mode 100644 index 000000000..377392cf1 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN1000.java @@ -0,0 +1,121 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITH the code smell: the child collection is fetched eagerly. + * + *

Models the client side of a JPA read where a parent entity is loaded together with a + * collection association mapped with {@code FetchType.EAGER}, while the application never + * accesses that collection. + * + *

This file is measured by EnergyTracer, whose Java runner compiles with a bare + * {@code javac } and runs with {@code java -cp }: no third-party + * library is reachable, hence the hand-rolled row source instead of JDBC/Hibernate. + * See README.md for what this does and does not measure. + * + *

The only difference with the compliant variant is the body of {@code loadOrder}. + */ +public class FetchTypeEagerN1000 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 1000; + + /** Parent reads performed by one JVM run. Calibrated so a run lasts ~300-500 ms, + * so that JVM startup (~100-400 ms, once per iteration) does not drown the signal. */ + private static final int PARENT_READS = 5000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // EAGER: the provider hydrates the whole collection now, used or not. + Set items = hydrateItems(p); + return new Order(id, reference, items); + } + + private static Set hydrateItems(int p) { + Set items = new HashSet<>(Math.max(16, CHILD_COUNT * 2)); + int base = p * CHILD_COUNT * CHILD_FIELDS; + for (int i = 0; i < CHILD_COUNT; i++) { + int o = base + i * CHILD_FIELDS; + items.add(new OrderItem( + CHILD_WIRE[o], + CHILD_WIRE[o + 1], + CHILD_WIRE[o + 2], + "ITEM-" + CHILD_WIRE[o])); + } + return items; + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN30.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN30.java new file mode 100644 index 000000000..c208892ce --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN30.java @@ -0,0 +1,121 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITH the code smell: the child collection is fetched eagerly. + * + *

Models the client side of a JPA read where a parent entity is loaded together with a + * collection association mapped with {@code FetchType.EAGER}, while the application never + * accesses that collection. + * + *

This file is measured by EnergyTracer, whose Java runner compiles with a bare + * {@code javac } and runs with {@code java -cp }: no third-party + * library is reachable, hence the hand-rolled row source instead of JDBC/Hibernate. + * See README.md for what this does and does not measure. + * + *

The only difference with the compliant variant is the body of {@code loadOrder}. + */ +public class FetchTypeEagerN30 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 30; + + /** Parent reads performed by one JVM run. Calibrated so a run lasts ~300-500 ms, + * so that JVM startup (~100-400 ms, once per iteration) does not drown the signal. */ + private static final int PARENT_READS = 166667; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // EAGER: the provider hydrates the whole collection now, used or not. + Set items = hydrateItems(p); + return new Order(id, reference, items); + } + + private static Set hydrateItems(int p) { + Set items = new HashSet<>(Math.max(16, CHILD_COUNT * 2)); + int base = p * CHILD_COUNT * CHILD_FIELDS; + for (int i = 0; i < CHILD_COUNT; i++) { + int o = base + i * CHILD_FIELDS; + items.add(new OrderItem( + CHILD_WIRE[o], + CHILD_WIRE[o + 1], + CHILD_WIRE[o + 2], + "ITEM-" + CHILD_WIRE[o])); + } + return items; + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN50.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN50.java new file mode 100644 index 000000000..ded6f7bf4 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN50.java @@ -0,0 +1,121 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITH the code smell: the child collection is fetched eagerly. + * + *

Models the client side of a JPA read where a parent entity is loaded together with a + * collection association mapped with {@code FetchType.EAGER}, while the application never + * accesses that collection. + * + *

This file is measured by EnergyTracer, whose Java runner compiles with a bare + * {@code javac } and runs with {@code java -cp }: no third-party + * library is reachable, hence the hand-rolled row source instead of JDBC/Hibernate. + * See README.md for what this does and does not measure. + * + *

The only difference with the compliant variant is the body of {@code loadOrder}. + */ +public class FetchTypeEagerN50 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 50; + + /** Parent reads performed by one JVM run. Calibrated so a run lasts ~300-500 ms, + * so that JVM startup (~100-400 ms, once per iteration) does not drown the signal. */ + private static final int PARENT_READS = 100000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // EAGER: the provider hydrates the whole collection now, used or not. + Set items = hydrateItems(p); + return new Order(id, reference, items); + } + + private static Set hydrateItems(int p) { + Set items = new HashSet<>(Math.max(16, CHILD_COUNT * 2)); + int base = p * CHILD_COUNT * CHILD_FIELDS; + for (int i = 0; i < CHILD_COUNT; i++) { + int o = base + i * CHILD_FIELDS; + items.add(new OrderItem( + CHILD_WIRE[o], + CHILD_WIRE[o + 1], + CHILD_WIRE[o + 2], + "ITEM-" + CHILD_WIRE[o])); + } + return items; + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN500.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN500.java new file mode 100644 index 000000000..1d41d0a23 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeEagerN500.java @@ -0,0 +1,121 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITH the code smell: the child collection is fetched eagerly. + * + *

Models the client side of a JPA read where a parent entity is loaded together with a + * collection association mapped with {@code FetchType.EAGER}, while the application never + * accesses that collection. + * + *

This file is measured by EnergyTracer, whose Java runner compiles with a bare + * {@code javac } and runs with {@code java -cp }: no third-party + * library is reachable, hence the hand-rolled row source instead of JDBC/Hibernate. + * See README.md for what this does and does not measure. + * + *

The only difference with the compliant variant is the body of {@code loadOrder}. + */ +public class FetchTypeEagerN500 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 500; + + /** Parent reads performed by one JVM run. Calibrated so a run lasts ~300-500 ms, + * so that JVM startup (~100-400 ms, once per iteration) does not drown the signal. */ + private static final int PARENT_READS = 10000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // EAGER: the provider hydrates the whole collection now, used or not. + Set items = hydrateItems(p); + return new Order(id, reference, items); + } + + private static Set hydrateItems(int p) { + Set items = new HashSet<>(Math.max(16, CHILD_COUNT * 2)); + int base = p * CHILD_COUNT * CHILD_FIELDS; + for (int i = 0; i < CHILD_COUNT; i++) { + int o = base + i * CHILD_FIELDS; + items.add(new OrderItem( + CHILD_WIRE[o], + CHILD_WIRE[o + 1], + CHILD_WIRE[o + 2], + "ITEM-" + CHILD_WIRE[o])); + } + return items; + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN10.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN10.java new file mode 100644 index 000000000..58e61ce2a --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN10.java @@ -0,0 +1,104 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITHOUT the code smell: the child collection is fetched lazily. + * + *

Strictly identical to the noncompliant variant except for the body of + * {@code loadOrder}: no call to {@code hydrateItems}. The empty {@code HashSet} + * stands for the lazy collection wrapper a real provider allocates, so the + * comparison stays conservative (the compliant variant is not free). + * + *

See README.md for what this does and does not measure. + */ +public class FetchTypeLazyN10 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 10; + + /** Parent reads performed by one JVM run. Must be identical to the eager variant. */ + private static final int PARENT_READS = 500000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. + * Allocated in both variants so the two share the same memory footprint. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // LAZY: only the collection wrapper is allocated. Rows are never fetched + // because the application never touches the collection. + Set items = new HashSet<>(); + return new Order(id, reference, items); + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN100.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN100.java new file mode 100644 index 000000000..3c1919900 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN100.java @@ -0,0 +1,104 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITHOUT the code smell: the child collection is fetched lazily. + * + *

Strictly identical to the noncompliant variant except for the body of + * {@code loadOrder}: no call to {@code hydrateItems}. The empty {@code HashSet} + * stands for the lazy collection wrapper a real provider allocates, so the + * comparison stays conservative (the compliant variant is not free). + * + *

See README.md for what this does and does not measure. + */ +public class FetchTypeLazyN100 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 100; + + /** Parent reads performed by one JVM run. Must be identical to the eager variant. */ + private static final int PARENT_READS = 50000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. + * Allocated in both variants so the two share the same memory footprint. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // LAZY: only the collection wrapper is allocated. Rows are never fetched + // because the application never touches the collection. + Set items = new HashSet<>(); + return new Order(id, reference, items); + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN1000.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN1000.java new file mode 100644 index 000000000..b13200bfd --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN1000.java @@ -0,0 +1,104 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITHOUT the code smell: the child collection is fetched lazily. + * + *

Strictly identical to the noncompliant variant except for the body of + * {@code loadOrder}: no call to {@code hydrateItems}. The empty {@code HashSet} + * stands for the lazy collection wrapper a real provider allocates, so the + * comparison stays conservative (the compliant variant is not free). + * + *

See README.md for what this does and does not measure. + */ +public class FetchTypeLazyN1000 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 1000; + + /** Parent reads performed by one JVM run. Must be identical to the eager variant. */ + private static final int PARENT_READS = 5000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. + * Allocated in both variants so the two share the same memory footprint. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // LAZY: only the collection wrapper is allocated. Rows are never fetched + // because the application never touches the collection. + Set items = new HashSet<>(); + return new Order(id, reference, items); + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN30.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN30.java new file mode 100644 index 000000000..8aa558dc4 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN30.java @@ -0,0 +1,104 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITHOUT the code smell: the child collection is fetched lazily. + * + *

Strictly identical to the noncompliant variant except for the body of + * {@code loadOrder}: no call to {@code hydrateItems}. The empty {@code HashSet} + * stands for the lazy collection wrapper a real provider allocates, so the + * comparison stays conservative (the compliant variant is not free). + * + *

See README.md for what this does and does not measure. + */ +public class FetchTypeLazyN30 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 30; + + /** Parent reads performed by one JVM run. Must be identical to the eager variant. */ + private static final int PARENT_READS = 166667; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. + * Allocated in both variants so the two share the same memory footprint. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // LAZY: only the collection wrapper is allocated. Rows are never fetched + // because the application never touches the collection. + Set items = new HashSet<>(); + return new Order(id, reference, items); + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN50.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN50.java new file mode 100644 index 000000000..562be4705 --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN50.java @@ -0,0 +1,104 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITHOUT the code smell: the child collection is fetched lazily. + * + *

Strictly identical to the noncompliant variant except for the body of + * {@code loadOrder}: no call to {@code hydrateItems}. The empty {@code HashSet} + * stands for the lazy collection wrapper a real provider allocates, so the + * comparison stays conservative (the compliant variant is not free). + * + *

See README.md for what this does and does not measure. + */ +public class FetchTypeLazyN50 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 50; + + /** Parent reads performed by one JVM run. Must be identical to the eager variant. */ + private static final int PARENT_READS = 100000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. + * Allocated in both variants so the two share the same memory footprint. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // LAZY: only the collection wrapper is allocated. Rows are never fetched + // because the application never touches the collection. + Set items = new HashSet<>(); + return new Order(id, reference, items); + } +} diff --git a/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN500.java b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN500.java new file mode 100644 index 000000000..4125b758d --- /dev/null +++ b/src/main/rules/S6904/java/analysis/level2-energytracer/generate/variants/FetchTypeLazyN500.java @@ -0,0 +1,104 @@ +import java.util.HashSet; +import java.util.Set; + +/** + * FetchType LAZY - variant WITHOUT the code smell: the child collection is fetched lazily. + * + *

Strictly identical to the noncompliant variant except for the body of + * {@code loadOrder}: no call to {@code hydrateItems}. The empty {@code HashSet} + * stands for the lazy collection wrapper a real provider allocates, so the + * comparison stays conservative (the compliant variant is not free). + * + *

See README.md for what this does and does not measure. + */ +public class FetchTypeLazyN500 { + + /** Number of child rows per parent: the collection size under test. */ + private static final int CHILD_COUNT = 500; + + /** Parent reads performed by one JVM run. Must be identical to the eager variant. */ + private static final int PARENT_READS = 10000; + + /** Distinct parent rows in the simulated table. */ + private static final int PARENT_ROWS = 200; + + private static final int CHILD_FIELDS = 3; + + /** Simulated persistence context. Keeps hydrated entities reachable, as a real + * provider does - and, incidentally, prevents the JIT from eliding the work. */ + private static final int CONTEXT_SIZE = 1024; + + /** Simulated wire buffer for parent rows: id, code. */ + private static final long[] PARENT_WIRE = new long[PARENT_ROWS * 2]; + + /** Simulated wire buffer for child rows: id, parentId, quantity. + * Allocated in both variants so the two share the same memory footprint. */ + private static final long[] CHILD_WIRE = new long[PARENT_ROWS * CHILD_COUNT * CHILD_FIELDS]; + + private static final Object[] CONTEXT = new Object[CONTEXT_SIZE]; + + /** Consumes the computed value so it cannot be optimised away. */ + static volatile long sink; + + static { + for (int i = 0; i < PARENT_ROWS; i++) { + PARENT_WIRE[i * 2] = i; + PARENT_WIRE[i * 2 + 1] = 1000L + i; + } + for (int i = 0; i < PARENT_ROWS * CHILD_COUNT; i++) { + int o = i * CHILD_FIELDS; + CHILD_WIRE[o] = i; + CHILD_WIRE[o + 1] = i / CHILD_COUNT; + CHILD_WIRE[o + 2] = 1 + (i % 7); + } + } + + static final class OrderItem { + final long id; + final long orderId; + final long quantity; + final String label; + + OrderItem(long id, long orderId, long quantity, String label) { + this.id = id; + this.orderId = orderId; + this.quantity = quantity; + this.label = label; + } + } + + static final class Order { + final long id; + final String reference; + final Set items; + + Order(long id, String reference, Set items) { + this.id = id; + this.reference = reference; + this.items = items; + } + } + + public static void main(String[] args) { + long acc = 0; + for (int r = 0; r < PARENT_READS; r++) { + Order order = loadOrder(r % PARENT_ROWS); + CONTEXT[r & (CONTEXT_SIZE - 1)] = order; + // The application only needs the parent. The collection is never accessed. + acc += order.id + order.reference.length(); + } + sink = acc; + if (sink == Long.MIN_VALUE) { + throw new IllegalStateException(); + } + } + + private static Order loadOrder(int p) { + long id = PARENT_WIRE[p * 2]; + String reference = "ORD-" + PARENT_WIRE[p * 2 + 1]; + // LAZY: only the collection wrapper is allocated. Rows are never fetched + // because the application never touches the collection. + Set items = new HashSet<>(); + return new Order(id, reference, items); + } +} diff --git a/src/main/rules/S6904/java/analysis/s6904-analysis-summary.md b/src/main/rules/S6904/java/analysis/s6904-analysis-summary.md new file mode 100644 index 000000000..c5b0b6a7c --- /dev/null +++ b/src/main/rules/S6904/java/analysis/s6904-analysis-summary.md @@ -0,0 +1,161 @@ +# PR #487 description β€” corrected against the actual current file changes + +*Reworked 6 August 2026. The previous version of this file corrected the "closing as +duplicate" framing but still reused the **Context**/**What this PR contains** text as +published at the top of [PR #487](https://github.com/green-code-initiative/creedengo-rules-specifications/pull/487)'s +conversation. That text describes the PR's *first* commit (`d111698`, "GCI116 - first init +from other PRs") β€” it was written before three more commits pivoted the approach. Checked +against the actual current file contents at the latest commit (`d92dbf7`) rather than the +stale top-of-page description:* + +- `CHANGELOG.md` (raw, current): adds β€” under `[Unreleased]` / `Added` β€” *"[#487] Add + complete analysis for FetchType issue for Java (sonarqube built-in rule S6904)"*. +- `RULES.md` (raw, current): the row for this rule still uses the **legacy key `CRJVM205`** + and now reads *"The rule already exists inside SonarQube : built-in rule 'java:S6904'"*, + with its Reference/Validation link pointing at PR #487. +- `src/main/rules/S6904/java/S6904.asciidoc` (new file, 106 lines, fully present in this + PR): opens with *"== Why is this rule isn't a 'GCIxxx' rule ? Because the rule already + exists as built-in rule inside SonarQube with number S6904. Some works were done by + different people. To not loose this work, we created this documentation to keep it."* + +**No `GCI116.json` or `GCI116.asciidoc` file exists in the current changeset.** That plan +was abandoned after the first commit. **"contenu" (What this PR contains) in the previous +draft was wrong** β€” it listed the GCI116 files from the abandoned plan. Fixed below. + +Two blocks: + +- **A** β€” internal record of everything changed in this pass (What this PR contains, + Context, and the removal of Implementation and Open question) +- **B** β€” the full corrected PR description, paste as is over the current one on #487 + +--- +--- + +# A β€” Internal record + +## What is fixed here + +- **"What this PR contains"** β€” rewritten to match the real current file list (`RULES.md`, + `CHANGELOG.md`, `S6904.asciidoc`, and the analysis harness/campaigns), dropping the + `GCI116.json` / `GCI116.asciidoc` bullets that no longer correspond to anything in the PR. +- **"Context"** β€” last sentence rewritten: no longer claims the PR "consolidates them into + a single specification... under the key `GCI116`." Now states the rule already exists as + the built-in `java:S6904`, that no new specification is added, and that `RULES.md`'s + `CRJVM205` entry is updated to point at the built-in rule instead β€” matching what + `RULES.md` and `CHANGELOG.md` actually say. +- **"Implementation"** β€” removed. It assumed a new rule was being implemented in + `creedengo-java`; that doesn't apply once the rule is recognised as already built into + SonarQube's engine. +- **"Open question"** β€” removed. It framed the 2024-01-01 core-team question as resolved by + *narrowing the scope*; the real resolution is that the rule already exists as a built-in, + which made the scope question moot rather than answered. +- The **"Measured impact"** section's forward reference to "see Open question below" was + removed along with that section, and reworded to stand on its own. + +--- +--- + +# B β€” Corrected PR description, paste as is + +```markdown +Supersedes PRs #122, #125, #155, #325 and resolves #98, #116, #118 + +### Context + +Four PRs have proposed this rule since April 2023 (#122, #125, #155, #325). None could be merged: +three of them target the old `java-plugin/` layout that no longer exists in this repository, and +they use four different rule keys (`EC80`, `CRJVM205`, `EC_CRJVM205`, `EC205`). On review, the rule +already exists inside SonarQube as the built-in rule `java:S6904`. This PR does not add a new +specification: it keeps the `CRJVM205` entry in `RULES.md`, updates it to point at the built-in +rule, and preserves the work already done as supporting analysis rather than letting it go to +waste β€” see **Measured impact** below. + +### What this PR contains + +- `RULES.md` β€” updates the `CRJVM205` row: instead of a new rule entry, it now states the rule + already exists inside SonarQube as the built-in rule `java:S6904`, with the Reference/Validation + link pointing at this PR. +- `CHANGELOG.md` β€” entry under `[Unreleased]` / `Added`: "Add complete analysis for FetchType issue + for Java (sonarqube built-in rule S6904)". +- `src/main/rules/S6904/java/S6904.asciidoc` β€” documentation explaining why this isn't a new + `GCIxxx` rule, plus the standard "why is this an issue," compliant/noncompliant examples, + exceptions and resources sections, and a pointer to the full measurement analysis. +- `src/main/rules/S6904/java/analysis/` β€” the two-level measurement harness (JPA/Hibernate + functional cost, then EnergyTracer hardware energy measurement) and two independent measurement + campaigns, added to answer the impact question the four superseded PRs left open. See + **Measured impact** below. + +### Scope decision + +The rule targets **collection associations only** (`@OneToMany`, `@ManyToMany`) and **only when +`fetch = FetchType.EAGER` is set explicitly**. Omitting `fetch` on those annotations is compliant, +since `LAZY` is already the JPA default β€” PR #155 reported those cases, which would have produced +false positives on the most common mapping in the domain. + +Single-valued associations (`@ManyToOne`, `@OneToOne`) are explicitly out of scope: their cost +profile is different and their real impact belongs to the N+1 selects problem (`CRJVM206`). + +### Measured impact + +None of the four superseded PRs provided any measurement of the actual cost β€” that gap is what +stalled the core-team discussion since April 2023. Two independent campaigns close +it, using a real Hibernate stack for functional cost and EnergyTracer's hardware counters +(Apple Silicon CPU/GPU/DRAM) for energy, 900 runs per variant per size across 30 measurement +phases. + +**Functional cost** (Hibernate 6.4.4 + H2, single run per size): + +| Children collection size | Extra rows hydrated | Time | Heap allocation | SQL statements | +|---:|---:|---:|---:|---:| +| 10 | +10 | x1.81 | x1.38 | unchanged | +| 100 | +100 | x3.73 | x4.37 | unchanged | +| 1000 | +1000 | x12.65 | x32.09 | unchanged | + +The statement count never changes β€” Hibernate resolves the eager collection with a join, so this +is a payload/hydration cost, confirming the N+1 exclusion in the scope decision above rather than +overlapping with it. + +**Energy cost** (two campaigns, six distinct collection sizes from 10 to 1000; three of them, +10/100/1000, measured independently in both): + +| Children collection size | Relative energy overhead (with smell) | Measured in | Sign test across 30 phases | +|---:|---:|---|:---:| +| 10 | 81.0–81.3% | campaigns 1 and 2 | p β‰ˆ 1.9Γ—10⁻⁹ | +| 30 | 83.9% | campaign 2 | p β‰ˆ 1.9Γ—10⁻⁹ | +| 50 | 85.8% | campaign 2 | p β‰ˆ 1.9Γ—10⁻⁹ | +| 100 | 86.3–86.7% | campaigns 1 and 2 | p β‰ˆ 1.9Γ—10⁻⁹ | +| 500 | 87.1% | campaign 2 | p β‰ˆ 1.9Γ—10⁻⁹ | +| 1000 | 90.1–90.7% | campaigns 1 and 2 | p β‰ˆ 1.9Γ—10⁻⁹ | + +Every one of the 30 measurement phases at every size points the same way, at both hardware +components (CPU, DRAM) and wall time, with strictly disjoint EAGER/LAZY distributions. The +relative overhead is already large at the smallest size tested (10 elements) and climbs to 90%+ +at 1000. The second campaign also located a genuine, honestly-disclosed anomaly in the +fine-grained scaling curve around 50–100 elements, which does not change this conclusion (see +campaign 2's document, Β§4–§5). + +Reproducibility across the two independent measurement sessions (different days) is strong: the +relative overhead never moved by more than 0.6 percentage points at the three sizes tested in +both campaigns, even though absolute joule readings drifted by up to 16% between sessions β€” +which is why every figure above is a relative, paired comparison rather than a raw energy total. + +Full detail, including the honest caveats (bimodal phases, the `CONTEXT_SIZE` confound, why the +tool's own p-values are not cited, the modest absolute yearly saving): `campaign1-interpretation.md` +and `campaign2-interpretation.md` under `src/main/rules/S6904/java/analysis/level2-energytracer/`. + +### Credits + +- [@AntoineMeheut](https://github.com/AntoineMeheut) and [@dirdr](https://github.com/dirdr) (Crjvm205 #122): ecological rationale, JPA default fetch types, test matrix +- [@kerimboukadida](https://github.com/kerimboukadida) (CRJVM205 - Forcer l'utilisation du FetchType LAZY sur les collections… #155): `@ManyToMany(EAGER)` example, real-project validation +- [@AndreBraga1](https://github.com/AndreBraga1) ([205][Java] Force usage of FetchType LAZY for collections on JPA entities #325): repository structure, CHANGELOG and RULES.md entries +- [@misterdz-mh](https://github.com/misterdz-mh) (CRJVM 205 : Forcer l'utilisation du FetchType LAZY sur les collections dans les Entity JPA #125): `orm` tag +- [@dedece35](https://github.com/dedece35) : complete Analysis (JPA impact / EnergyTracer measurements) +``` + +--- + +## Before posting + +- [ ] Confirm the analysis-harness bullet under **What this PR contains** matches how you + want `S6904/java/analysis/` described +- [ ] **Review and validate before posting** β€” this replaces the live description on #487