Chapter 21: Key Takeaways

AI-Assisted Testing Strategies — Summary Card

AI-generated code requires more testing, not less. You lack the mental model that comes from writing code yourself, creating a "trust gap" that only testing can bridge.
Common AI failure modes are predictable. Happy-path bias, plausible but incorrect logic, inconsistent error handling, stale patterns, and context drift are the most frequent issues. Design your tests to target these patterns specifically.
pytest is the foundation. Use fixtures for setup and teardown, @pytest.mark.parametrize for testing multiple inputs efficiently, markers for organizing test categories, and conftest.py for sharing fixtures across files.
Follow the testing pyramid. Build the majority of your tests (70-80%) as fast unit tests, a moderate number (15-25%) as integration tests, and a small number (5-10%) as end-to-end tests. This ratio maximizes confidence while minimizing test execution time.
Property-based testing with Hypothesis is your strongest weapon against AI bugs. Instead of testing specific examples, define properties that should hold for all valid inputs. Hypothesis generates hundreds of random inputs and finds edge cases you would never think to test manually.
Use TDD with AI for maximum control. Write tests first to define the specification, then have the AI implement the code. This workflow keeps you in charge of what the code does while leveraging AI for how it does it.
Mock at boundaries, not internally. Replace external dependencies (APIs, databases, file systems) with test doubles, but let internal logic run for real. Over-mocking creates tests that verify mock configurations rather than actual code behavior.
Coverage is a guide, not a goal. Use coverage reports to find important untested code, but do not chase arbitrary percentage targets. A test with 80% coverage and strong assertions is more valuable than 100% coverage with weak assertions.
Mutation testing reveals test suite weaknesses. If you modify source code (flip a comparison, remove a return) and no test fails, your tests have a blind spot. Periodically run mutation testing on critical code paths.
Automate everything through CI/CD. Tests that do not run consistently provide no value. Set up continuous integration to run linting, type checking, unit tests, integration tests, and coverage reporting on every code change.
Prompt AI effectively for test generation. Specify the framework (pytest), techniques (parametrize, fixtures), edge cases to cover (empty input, None, boundary values), and code style (docstrings, type hints). Vague prompts produce weak tests.
Write strong assertions. Verify exact expected values, not just that results are non-None or the right type. Weak assertions let bugs pass undetected.
Test error handling explicitly. AI-generated code often has incomplete or inconsistent error handling. Write tests that deliberately trigger every error path and verify appropriate exceptions are raised with informative messages.
Maintain your tests like production code. Delete obsolete tests, refactor for readability, keep tests independent of each other, and ensure they run fast. Neglected test suites lose their value over time.
Your tests are the specification. This is the most important mindset shift in AI-assisted development. The AI writes implementation; you write the contract that implementation must satisfy. Tests are that contract.