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python-mock-isolation-design

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Use Python mocks and fakes as a design tool without losing behavioral confidence. Use when testing external dependencies, choosing monkeypatch versus unittest.mock.patch, isolating slow boundaries, avoiding mock-heavy tests, interpreting mock call assertions, or refactoring toward clearer dependency seams.

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Python Mock Isolation Design

Use this skill when replacing a dependency in a Python test changes what the test proves. Mocks are useful at slow, nondeterministic, or external boundaries; they become harmful when they replace the behavior the test is supposed to validate.

Source Traceability

Primary source: Harry Percival, Test-Driven Development with Python, 3rd ed. Guidance is transformed and paraphrased from chapters 20, 21, 27, and Appendix A, especially manual monkeypatching, unittest.mock.patch, mock coupling, call argument inspection, test isolation, and the architectural route out of mock-heavy suites.

Workflow

  1. Name the boundary.

    • External service, email, clock, filesystem, network, framework adapter, or expensive side effect.
    • If the dependency is local domain logic, prefer real code.

  2. Choose the replacement.

    • Use a fake when state and behavior are simple and meaningful.
    • Use patch when replacing a collaborator looked up by the module under test.
    • Use monkeypatching sparingly and restore state automatically.
    • Use autospec/spec when interface drift matters.

  3. Keep behavior assertions primary.

    • Assert user-visible or domain-visible outcomes first.
    • Assert mock calls only when the interaction is the contract.
    • Inspect call arguments when they clarify the behavior, not as a substitute for it.

  4. Watch for design feedback.

    • If setup requires many mocks, the code may have hidden dependencies.
    • If tests break on harmless refactors, the test is too coupled to implementation.
    • Consider introducing a boundary interface, service object, or functional core.

Read mock-isolation-patterns.md for replacement choices and smell handling.

Decision Rules

  • Patch where the code under test looks up the dependency.
  • Prefer fakes for small stable protocols.
  • Use mock.return_value and side_effect deliberately; do not let default mocks create imaginary object graphs.
  • Avoid broad MagicMock objects without specs for important interfaces.
  • Keep at least one integration or contract test for each important boundary.

Guardrails

  • Do not mock Django settings, HTTP, time, or command output by hand if a framework helper exists; use django-targeted-mocking for Django-specific boundaries.
  • Do not assert only that a mocked method was called unless that call is the behavior.
  • Do not use mocks as the primary strategy for making all tests fast; architecture should carry that work.
  • Do not keep mocks that prevent refactoring from changing implementation safely.

Verification

Before finishing, record:

  • Boundary being replaced and why.
  • Fake, patch, or real dependency choice.
  • Behavior assertion that proves the outcome.
  • Mock interaction assertion only when interaction is the contract.
  • Integration/contract coverage for the real boundary.