kata-python-debug

To debug Python bugs using a test-driven approach, you write a minimal failing regression test that reproduces the reported issue, then implement a targeted fix to make it pass. This ensures the bug is truly understood before changes are made.

The best way to find the root cause of a Python application failure is to trace data flow from the trigger to the failure point. This process narrows the issue to a unit-level reproduction and categorizes it as logic, validation, race, mapping, or infrastructure.

To verify a Python bug fix without chasing symptoms, you implement a minimal targeted correction and run the regression test alongside the full test suite. You also verify code quality by passing Ruff and mypy checks.

Yes, this debugging workflow requires stack traces and reproducible failures across endpoints, functions, and events. These behavioral expectations are necessary to create the minimal failing regression test that starts the process.

Yes, you can use Ruff and mypy to validate your Python bug fixes. After applying the minimal fix to pass the regression test and full test suite, these linters and type checkers confirm the code maintains quality standards.

You should write a failing regression test before fixing Python code to guarantee the bug is accurately reproduced. This test isolates the true root cause and prevents symptom-chasing by providing a clear, measurable target for the minimal fix.