Create Pytest Fixtures and Parametrized Test Suites for Python Applications
Generate a complete pytest test suite with reusable fixtures, parametrized test cases, and proper organization for Python projects.
📝 The Prompt
Write a comprehensive pytest test suite for [MODULE_OR_FUNCTION_NAME] in a [APPLICATION_TYPE] Python application. The module handles [BRIEF_FUNCTIONALITY_DESCRIPTION].
Generate the following:
1. **Fixtures** (in `conftest.py`):
- A session-scoped fixture for [EXPENSIVE_RESOURCE] (e.g., database connection, API client) with proper setup and teardown using `yield`
- A function-scoped fixture that provides [TEST_DATA_DESCRIPTION] with realistic sample data
- A fixture that mocks [EXTERNAL_DEPENDENCY] using `unittest.mock.patch` or `pytest-mock`
- A fixture factory using `@pytest.fixture` with parameters for creating [ENTITY_NAME] objects with customizable attributes
2. **Parametrized Tests**:
- Use `@pytest.mark.parametrize` to test [FUNCTION_NAME] with at least 6 cases covering: valid inputs, boundary values, empty/null inputs, type mismatches, and [DOMAIN_SPECIFIC_EDGE_CASE]
- Include indirect parametrization for fixture-based test data
- Add descriptive test IDs using `pytest.param(..., id='descriptive_name')`
3. **Test Organization**:
- Group tests in a class `Test[FeatureName]` with clear docstrings
- Include `@pytest.mark.slow` for integration-style tests
- Add `@pytest.mark.xfail` for known issues with [KNOWN_ISSUE_DESCRIPTION]
4. **Assertions**: Use specific assertions (`assert x == y`, `pytest.raises`, `pytest.approx`) rather than bare `assert`. Include assertion messages for complex checks.
Assume these dependencies are installed: [DEPENDENCY_LIST]. Follow the Arrange-Act-Assert pattern consistently.
💡 Tips for Better Results
Describe the actual function signatures and return types of what you're testing so the AI generates accurate assertions. List your real external dependencies in [EXTERNAL_DEPENDENCY] to get useful mock configurations. Review generated edge cases and add domain-specific ones the AI might miss.
🎯 Use Cases
Python developers writing or expanding test coverage for existing modules who need well-structured, maintainable test suites with proper fixture management.