python-testing-patterns

To write unit, integration, and functional tests in Python using pytest, create a file starting with test_ and define functions prefixed with test_. This Skill provides guidance on structuring these tests to ensure robust coverage and reliable execution across your application.

The best way to set up test infrastructure with fixtures and mocking in pytest involves using pytest fixtures for managing test setup states and unittest.mock to simulate external services. This Skill guides you through effective parameterization and mocking strategies for maintaining reliable test environments.

Yes, this pytest testing guidance covers async code and exception handling. It requires pytest-asyncio for testing asynchronous Python code and provides comprehensive patterns for verifying expected exceptions, ensuring your async applications and error handling logic maintain code quality.

Yes, you can use hypothesis for property-based testing alongside pytest. This Skill includes hypothesis as a dependency to implement advanced testing patterns, allowing you to automatically generate test cases and validate code behavior against a wide range of inputs.

To integrate pytest test suites into a CI/CD pipeline, you utilize pytest-cov to measure code coverage and generate reports. This Skill provides best practices for test organization and reporting, ensuring your continuous integration workflows reliably validate code quality.

You should implement Test-Driven Development for Python APIs when developing new features to ensure reliability from the start. This Skill covers TDD best practices, guiding you to mock external services and test various response scenarios before finalizing implementation.