The part that tripped me up was the narration requirement.
Start by running the tests to see the failures, then systematically trace each failure to its root cause using the error messages and code inspection. Fix one bug at a time, re-running tests after each change to confirm progress and avoid introducing new issues. Narrate your hypotheses, the evidence you gather, and your reasoning for each fix.
Pro tip: Before diving into fixes, take a moment to understand the codebase's structure and the test suite's purpose—this context often reveals patterns behind the bugs and shows you prioritize understanding over speed.
Run the test suite to see all failures, then read the error messages and stack traces carefully to form initial hypotheses about what's wrong.
For each failure, inspect the relevant code, add debug prints or use a debugger if needed, and trace the logic to pinpoint the exact cause.
Make a minimal fix for one bug, re-run the tests to confirm it resolves that failure and doesn't break others, then move to the next.
After all tests pass, review your changes for quality, consider edge cases, and ensure the fixes are clean and maintainable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.