testing

Unit tests with pytest isolate individual functions or methods using AAA-pattern tests (arrange, act, assert). Create test files prefixed with `test_`, define test functions, and run `pytest` to execute them. pytest handles fixture setup, parameterization, and mocking through built-in decorators, making it the standard for Python unit testing.

Unit tests verify individual functions in isolation; integration tests check how components work together; end-to-end tests validate complete workflows from user input to output. Each tests at a different scope: unit tests are fast and deterministic, integration tests catch cross-module issues, and e2e tests confirm the system works as a whole.

Mocking replaces real external services, APIs, or database calls with controlled fake implementations. Use pytest fixtures combined with mocking libraries to define mock behavior, control return values, and verify interactions. This ensures tests run fast, deterministically, and independently without requiring actual external services.

Yes. Testing frameworks support async test functions and provide timeout mechanisms to catch hanging operations. Define async fixtures and test functions, configure timeout values for long-running tasks, and use event-loop handling to manage concurrent operations while keeping tests deterministic and edge-case coverage clear.

Test coverage tracks what percentage of your code is exercised by tests. Use coverage tools to identify untested lines, set coverage targets (typically 70–90%), and prioritize testing high-risk areas like public APIs and edge cases. Coverage reports guide strategy and ensure regressions are caught consistently.

Design tests around public interfaces and contract requirements rather than implementation details. Create framework-agnostic test strategies using fixtures to mock dependencies, then apply language and framework-specific test tools (pytest for Python, Jest for JavaScript) to generate uniform test suites covering edge cases and API contracts.