This is a comprehensive API testing framework designed to systematically identify and report defects in web service interfaces using Python, Playwright, and advanced testing methodologies.

• Language: Python 3.10+
• Virtual Environment: uv
• Testing Framework:
  • Pytest
  • Playwright
• Validation:
  • Pydantic
• Linting: Ruff
• Pre-commit Hooks: pre-commit
• Reporting: Allure
• Parallel Execution: pytest-xdist

• Python 3.10+
• uv
• pip

git clone https://github.com/ZhikharevAl/qa-hackathon-raptors.git
cd qa-hackathon-raptors

uv venv  # Creates a new virtual environment
source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

uv pip install -r requirements.txt
uv pip install pre-commit
pre-commit install

Create a .env file with necessary credentials:

API_TOKEN=your_api_token_here

ruff check .  # Check for linting issues
ruff format .  # Format code

Automatically run before each commit:

• Linting
• Formatting
• Type checking
• Other configured checks

• Strict type validation
• Data parsing
• Schema generation
• Runtime type checking

from pydantic import BaseModel, Field

class UserResponse(BaseModel):
    uuid: str = Field(..., description="UUID of the user")
    email: str = Field(..., description="Email address of the user")
    name: str = Field(..., description="Name of the user")
    nickname: str = Field(..., description="Nickname of the user")

pytest

pytest -n auto  # Automatically detect available CPU cores

pytest tests/test_users.py

pytest --alluredir=allure-results

allure serve allure-results

project-root/
│
├── tests/               # Test suites
│   ├── test_users.py
│   └── ...
│
├── services/            # API service classes
│   ├── users/
│   │   └── models/     # Pydantic models
│   └── ...
│
├── config/              # Configuration files
├── utils/               # Utility modules
├── requirements.txt     # Dependency list
└── .pre-commit-config.yaml  # Pre-commit configuration

 pytest --cov=tests/

• Use uv for dependency management
• Utilize Ruff for consistent code quality
• Leverage pre-commit hooks
• Use Pydantic for robust data validation
• Create comprehensive Pydantic models
• Use fixtures for test setup/teardown
• Parameterize tests
• Validate response schemas
• coverage report

• Refactor tests to use fixtures
• 🎯 Target: 90%+ test coverage
• Dynamic test data generation

MIT