python-quality-testing

Hypothesis property-based tests use generated data strategies to automatically probe edge cases like shape errors and NaNs. This approach provides custom strategy recipes for tensors, DataFrames, and nested configs to catch silent failures that standard unit tests miss.

Mutation testing with mutmut deliberately alters code to see if existing tests fail. It measures the mutation kill rate to surface untested logic, providing configuration, result interpretation, and CI integration to assess and improve overall test robustness.

Auditing try-except blocks requires an exception-audit checklist to harden error handling, while contract docstring templates document preconditions and postconditions. This combination surfaces silent failures and makes error modes actionable before deployment.

Yes, this approach specifically targets ML and data-processing codebases by applying type annotations, advanced typing patterns, and tailored ruff configuration rules. It addresses pipeline fragility through pre-mortem prompts and structured code quality checks.

Tightening public APIs involves applying a type annotation checklist and advanced typing patterns to preserve type information across functions. This reduces brittle behavior by ensuring type safety and clarifying expected inputs and outputs.

Property-based testing generates hundreds of input variations automatically, detecting edge cases like NaNs and shape errors that manually written unit tests miss. It strengthens test robustness by verifying code properties hold across wide data ranges.