python-testing

pytest fixtures reuse setup logic and isolate external dependencies by injecting mocked service calls into your tests. This allows you to verify behavior and test exceptions without relying on live APIs or databases during execution.

TDD sequencing in Python follows the red-green-refactor practice: write a failing test, implement the minimum code to pass it, then refine the structure. This approach catches bugs early and keeps your codebase safe to change.

Yes, pytest supports testing async code and side effects by combining fixtures with mocking. You can isolate external dependencies, verify asynchronous behavior, and assert expected side effects for reliable end-to-end testing.

Setting coverage targets involves configuring pytest to measure execution across critical paths and broader regression safety. You define minimum coverage expectations to ensure maintainable and reliable verification before release.

Yes, pytest parametrization allows you to run the same test logic against multiple API inputs. It helps create clear, maintainable tests by defining markers and test cases that verify behavior across various data scenarios.

Mocking external services matters because it isolates integration dependencies, preventing live network calls from making tests flaky. It ensures your tests verify actual application behavior and exception handling reliably and consistently.