qa-hiberus-lab

A Python + Playwright automation framework built from scratch as a hands-on learning project, targeting the Hiberus Automation Engineer Senior role requirements.

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Prerequisites

• Python 3.12+ (project developed on 3.14.4)
• Git
• A terminal (Linux/macOS) or WSL (Windows)
• A GitHub account (for CI/CD in Section 3)

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Installation

1. Clone the repository

git clone https://github.com/somosamb/qa-hiberus-lab.git
cd qa-hiberus-lab

2. Create and activate the virtual environment

# Create the virtual environment in the .venv folder
python3 -m venv .venv

# Activate it (Linux/macOS)
source .venv/bin/activate

# Activate it (Windows)
.venv\Scripts\activate

3. Install Python dependencies

pip install -r requirements.txt

4. Install Playwright browsers

# Downloads Chromium, Firefox, and WebKit binaries
playwright install

Note: On Ubuntu 26.04, Playwright's bundled binaries are not yet supported. The project is configured to use the system-installed Brave browser as a drop-in replacement. No extra steps needed — conftest.py handles this automatically.

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Running the Tests

Run the full test suite

pytest

Run a specific test file

pytest tests/test_login.py

Run a specific test by name

pytest tests/test_login.py::test_valid_login

Run with verbose output

pytest -v

Run and generate an HTML report

pytest --html=reports/report.html --self-contained-html

Run tests in headed mode (see the browser)

pytest --headed

Run tests in headed mode with slow motion

# 1000 = 1 second delay between every action — useful for visual debugging
pytest --headed --slowmo 1000

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Project Structure

qa-hiberus-lab/
├── .github/
│   └── workflows/
│       └── ci.yml              # GitHub Actions CI/CD pipeline
├── pages/                      # Page Object Model (POM) layer
│   ├── __init__.py
│   ├── login_page.py           # Encapsulates login page actions and locators
│   ├── inventory_page.py       # Encapsulates inventory/product page behaviour
│   └── cart_page.py            # Encapsulates shopping cart page behaviour
├── tests/
│   ├── __init__.py
│   ├── test_login.py           # Login flow scenarios
│   ├── test_inventory.py       # Product listing, sorting, add-to-cart
│   ├── test_cart.py            # End-to-end cart and checkout validation
│   └── test_api.py             # REST API tests using requests library
├── data/
│   └── users.json              # Test user data for data-driven tests
├── Quiz/
│   └── Phase1.html             # Interactive HTML quiz for Phase 1 concepts
├── conftest.py                 # Shared pytest fixtures (browser, page, base_url)
├── pytest.ini                  # pytest configuration
├── requirements.txt            # All Python dependencies with pinned versions
├── Plan.md                     # Full learning plan and gap analysis
└── README.md                   # This file

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Architecture: Page Object Model (POM)

Each page of the application under test is represented by a Python class. The class owns all locators and all interactions for that page. Tests never interact with the browser directly — they always go through a page class.

tests/test_login.py
    └── calls → pages/login_page.py (LoginPage class)
                    └── uses → Playwright page object (browser interaction)

Why POM?

• Locator changes require updates in one place only
• Tests read like business scenarios, not like CSS selectors
• Reusable actions across multiple test files

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Key Concepts Practiced

Concept	Where applied
OOP (classes, inheritance)	All pages/*.py classes
Page Object Model	pages/ layer
Login Flows	tests/test_login.py
UI Testing	tests/test_inventory.py
Data structures (list/dict)	data/users.json + parametrize
Git (branches, commits)	Full project version control
Playwright	All UI tests
Python scripting	All test and page files
API Testing	tests/test_api.py
CI/CD pipeline	.github/workflows/ci.yml
HTML test reports	pytest-html output

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Test Data

Test users are stored in data/users.json and loaded in conftest.py. This demonstrates use of Python data structures (dicts, lists) applied to test automation.

{
  "valid_users": [
    { "username": "standard_user", "password": "secret_sauce" }
  ],
  "invalid_users": [
    { "username": "wrong_user", "password": "wrong_pass" }
  ],
  "locked_users": [
    { "username": "locked_out_user", "password": "secret_sauce" }
  ]
}

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CI/CD Pipeline (Section 3)

The GitHub Actions pipeline in .github/workflows/ci.yml runs automatically on every push and pull request to main.

Pipeline steps:

1. Checkout code
2. Set up Python 3.12
3. Install dependencies
4. Install Playwright browsers
5. Run pytest
6. Upload HTML report as a downloadable artifact

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Learning Sections

Section	Focus	Key output
1	Foundation	POM skeleton + login tests passing
2	Coverage + API	Full suite + data-driven + API tests
3	CI/CD	Green pipeline on GitHub Actions
4	Advanced (mobile + TS)	Mobile emulation + TypeScript tests

See Plan.md for the full detailed breakdown.

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Dependencies

Package	Purpose
pytest	Test runner and assertion framework
playwright	Browser automation (Chromium/Firefox/WebKit)
pytest-playwright	Playwright fixtures for pytest
requests	HTTP client for API testing
pytest-html	HTML test report generation

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Git Workflow

# Start a new feature branch (one branch per section or feature)
git checkout -b feature/section-1-login-tests

# Stage and commit your work
git add .
git commit -m "feat: add LoginPage POM class and login test suite"

# Push to remote
git push origin feature/section-1-login-tests

# Merge via pull request on GitHub

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Resources

• Playwright Python Docs
• pytest Docs
• SauceDemo (app under test)
• reqres.in (API under test)
• ISTQB Glossary