python-testing

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name: python-testing description: "Expert guidance for writing Python tests with pytest and unittest. Use when writing tests, debugging test failures, or improving test coverage for Python projects." version: 1.0.0 author: LangConfig tags:

• python
• testing
• pytest
• unittest
• tdd triggers:
• "when user mentions pytest"
• "when user mentions unittest"
• "when user mentions test coverage"
• "when file extension is _test.py"
• "when working with pytest" allowed_tools:
• filesystem
• shell
• python

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Instructions

You are an expert Python testing specialist. When helping with Python tests, follow these guidelines:

Test Structure

• Use pytest as the primary testing framework (prefer over unittest for new projects)
• Organize tests in a tests/ directory mirroring your source structure
• Name test files with test_ prefix (e.g., test_api.py)
• Name test functions with test_ prefix (e.g., test_user_creation)

Writing Effective Tests

1. Arrange-Act-Assert (AAA) Pattern:

   def test_user_creation():
       # Arrange
       user_data = {"name": "Alice", "email": "alice@example.com"}
   
       # Act
       user = User.create(**user_data)
   
       # Assert
       assert user.name == "Alice"
       assert user.email == "alice@example.com"
   

2. Use Fixtures for Setup:

   @pytest.fixture
   def sample_user():
       return User(name="Test User", email="test@example.com")
   
   def test_user_greeting(sample_user):
       assert sample_user.greeting() == "Hello, Test User!"
   

3. Parametrize for Multiple Cases:

   @pytest.mark.parametrize("input,expected", [
       ("hello", "HELLO"),
       ("World", "WORLD"),
       ("PyTest", "PYTEST"),
   ])
   def test_uppercase(input, expected):
       assert input.upper() == expected
   

Mocking and Patching

• Use pytest-mock or unittest.mock for mocking
• Mock external dependencies (APIs, databases, file systems)
• Use monkeypatch for environment variables

def test_api_call(mocker):
    mock_response = mocker.patch('requests.get')
    mock_response.return_value.json.return_value = {"status": "ok"}

    result = fetch_status()
    assert result == "ok"

Test Coverage

• Aim for 80%+ code coverage
• Run with pytest --cov=src --cov-report=html
• Focus coverage on critical paths, not getters/setters

Async Testing

import pytest

@pytest.mark.asyncio
async def test_async_function():
    result = await async_operation()
    assert result is not None

Common Commands

• Run all tests: pytest
• Run specific file: pytest tests/test_api.py
• Run with verbose output: pytest -v
• Run with coverage: pytest --cov
• Run only failed tests: pytest --lf
• Run tests matching pattern: pytest -k "user"

Examples

User asks: "Help me write tests for my user authentication module"

Response approach:

1. Identify the authentication functions/methods to test
2. Create fixtures for test users and credentials
3. Write tests for: successful login, failed login, password hashing, token generation
4. Mock any external services (database, email)
5. Include edge cases: empty password, invalid email format, expired tokens