python-testing

name	python-testing
description	Python testing patterns with pytest: TDD loop, fixtures, parametrization, mocking, test organization, async testing, coverage, and CI hygiene. Use when writing or reviewing Python tests to improve correctness and reduce flakiness.
license	MIT
compatibility	Python 3.11+ (guidance baseline). Tools: pytest. Optional: pytest-cov, pytest-asyncio, hypothesis.
metadata	{"author":"AeonDave","version":"1.1"}

Python Testing

High-signal guidance for writing tests that are deterministic, readable, and maintainable.

When to activate

• Adding tests (unit/integration) for new or existing code
• Refactoring tests to reduce flakiness
• Designing fixtures and test organization
• Adding coverage, CI checks, or async tests

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Outcome expectations

• Tests are deterministic and pass reliably in CI.
• Coverage is meaningful; assertions test behavior, not implementation details.
• Flaky tests are rare and quickly diagnosed (fixtures, mocking, timing).
• New developers can understand test intent within 30 seconds of reading.

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Recommended triage workflow

For new code:

1. Write a failing test (red).
2. Implement minimal code to pass (green).
3. Refactor with tests staying green.

For flaky/failing tests:

1. Check for shared state (mocks, fixtures, filesystem).
2. Check for timing assumptions (avoid sleep, use synchronization).
3. Check mock patch targets (patch as used, not where defined).
4. Isolate to minimal reproduction in separate test.

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Core rules

• Prefer small unit tests for logic; use integration tests for boundaries.
• Use pytest fixtures to remove duplication, but avoid fixture overengineering.
• Avoid sleeping in tests; synchronize via conditions/events.
• Mock at boundaries (network, time, DB), not everywhere.
• Coverage is a signal: aim for meaningful assertions, not line-hits.
• Do not add production hooks solely for tests; use real seams or dependency injection.
• Pair with test-driven-development when implementing persistent code or bug fixes test-first.
• If mocks, timing, or flakes dominate, pair with testing-reliability; if the root cause is unclear, pair with systematic-debugging.

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Resources

Load on demand:

• references/tdd-and-structure.md — TDD loop, naming, organizing tests
• references/fixtures-parametrize.md — fixtures (scopes, autouse), parametrization patterns
• references/mocking.md — unittest.mock, patching correctly, async mocks
• references/async.md — pytest-asyncio patterns and pitfalls
• references/coverage-ci.md — pytest-cov, coverage hygiene, CI tips
• references/commands.md — common pytest commands and selectors