python-testing

Pytest fixtures can be scoped to function, module, or session levels to manage test data lifecycle reliably. Organizing shared fixtures in conftest.py files enables scalable test layout and ensures reusable, isolated test data across large projects.

Async tests with pytest-asyncio execute asynchronous coroutines natively within pytest, ensuring proper teardown after completion. Using the marker on test functions manages the event loop lifecycle, validating asyncio code behavior while maintaining standard assertion mechanics.

Parametrization in pytest allows running a single test function against multiple inputs by defining variable sets and expected outcomes. This approach eliminates redundant test code, systematically covers diverse scenarios, and simplifies maintaining comprehensive test coverage.

Autospec with unittest.mock validates mock objects against the real object's specification, preventing silent failures from missing attributes. This approach isolates the system under test by replacing dependencies with type-safe mocks that enforce correct API usage during test execution.

The TDD workflow starts by writing a failing test (RED), implementing minimal code to pass it (GREEN), and then refactoring the codebase cleanly (REFACTOR). This iterative cycle ensures test coverage drives design decisions, producing maintainable Python modules.

Pytest markers categorize tests across unit, integration, and end-to-end scenarios to selectively run specific test suites. Markers combined with conftest.py organization provide a scalable test layout, allowing targeted execution and efficient test management for complex applications.