python-testing

Writing pytest tests using TDD involves following the RED → GREEN → REFACTOR cycle to drive correct behavior from tests before implementation. This approach enforces discipline by writing clear assertions, structuring tests logically, and using fixtures to manage setup and teardown.

The best way to mock external dependencies in pytest is through isolated mocking to ensure tests remain fast and deterministic. This allows you to validate backend module behavior across edge cases without relying on actual external services or network calls.

pytest supports async testing scenarios and allows you to verify expected failures and exception behavior using the pytest.raises context manager. This ensures your test suite accurately captures and validates error conditions in asynchronous code.

Parameterizing tests in pytest allows you to run the same test logic against multiple input combinations for broader coverage. You can select specific test subsets for execution by applying and filtering with marker-based selection.

Measuring test coverage in pytest is done using the pytest-cov plugin to quantify progress and validate that your suite meets an 80%+ coverage target. This ensures your automated tests provide confidence that code behaves correctly across edge cases.