How Developers Use pytest and Hypothesis to Catch Flaws in AI-Written Python Code

AI coding assistants can produce Python code that appears correct but contains subtle bugs, partly because the tests they generate share the same flawed assumptions as the code itself. A software developer has outlined a multi-layer testing strategy that uses property-based testing with Hypothesis, Pydantic schema snapshots, and mutation testing to independently verify AI-generated logic. The approach requires developers to write test properties by hand, ensuring that human judgment — not the AI model — defines what correct behavior looks like. Schema snapshots stored in version control flag unintended contract changes, such as a field type silently shifting from integer to float, before they reach production. Mutation testing tools like mutmut are run nightly on critical modules to expose gaps in test coverage that standard metrics would otherwise miss.