Create Pytest Test Suite with Fixtures and Parametrize Decorators
Generate a well-structured Pytest suite featuring reusable fixtures, parametrized tests, markers, and proper mocking patterns.
📝 The Prompt
Write a complete Pytest test suite for a Python module named [MODULE_NAME].py that implements [MODULE_PURPOSE]. The module contains these classes/functions:
[LIST_CLASSES_AND_FUNCTIONS_WITH_SIGNATURES]
Generate the test suite with the following components:
1. **Conftest.py Fixtures**:
- Create reusable fixtures for [SHARED_RESOURCES] (e.g., database session, API client, temporary files)
- Use appropriate scopes: function, class, module, or session — and explain why each scope was chosen
- Implement yield-based fixtures with proper teardown/cleanup logic
- Add a fixture that returns a factory function for creating [TEST_ENTITY] with customizable attributes
2. **Parametrized Test Cases**:
- Use @pytest.mark.parametrize for [FUNCTION_TO_PARAMETRIZE] with at least 5 input/output combinations including:
- Typical valid inputs
- Boundary values (min, max, zero, empty)
- Invalid inputs that should raise [EXPECTED_EXCEPTION]
- Use pytest.param() with id= for readable test IDs
- Stack multiple parametrize decorators for combinatorial testing where [COMBINATORIAL_SCENARIO]
3. **Test Organization**:
- Group related tests in classes prefixed with Test (e.g., TestUserService)
- Use markers: @pytest.mark.slow, @pytest.mark.integration, and custom marker @pytest.mark.[CUSTOM_MARKER]
- Include tests that use [MOCK_LIBRARY] (unittest.mock or pytest-mock) to patch [EXTERNAL_DEPENDENCY]
4. **Assertion Patterns**:
- Use pytest.raises() as context manager with match= for exception messages
- Use pytest.approx() for floating-point comparisons where applicable
- Add caplog fixture usage for testing log output from [LOGGING_FUNCTION]
5. **Configuration**: Provide a minimal pytest.ini or pyproject.toml [tool.pytest.ini_options] section with marker registrations and default flags.
Include docstrings for each test explaining the scenario being validated.
💡 Tips for Better Results
Include the actual function signatures and return types so the AI generates accurate assertions and parametrize tuples. Specify whether you're using pytest-mock or unittest.mock for consistent patching syntax. Mention your Python version to avoid incompatible syntax like match groups in pytest.raises().
🎯 Use Cases
Python developers and QA engineers use this when designing scalable, maintainable test suites for complex business logic with many input variations and shared setup requirements.