The part that tripped me up wasn't the bugs themselves, it was figuring out how to prompt the AI in a way that actually moved things forward instead of just generating plausible-sounding nonsense.
Start by reproducing the bug and understanding the expected behavior from the test suite, then use the AI assistant to help trace the root cause and propose fixes. Prioritize fixes that address the root cause rather than symptoms, and verify each change with the relevant tests. Finally, run the full test suite to ensure no regressions.
Pro tip: Use the AI assistant to generate hypotheses about the bug, but always validate them with your own reasoning and the test results. Keep a log of what you tried and the outcomes to avoid repeating mistakes and to demonstrate a systematic approach.
Run the failing tests to see the error messages and stack traces. Read the test code to understand the expected behavior and identify the specific failing scenario.
Use the AI assistant to help analyze the code paths involved, but narrow down the root cause by adding logging or using a debugger. Formulate a hypothesis about the underlying issue.
Apply a targeted fix for the root cause, then run the specific failing test to confirm it passes. Repeat for any additional failures, ensuring each fix is validated before moving on.
Execute the entire test suite to ensure all tests pass and no regressions were introduced. Address any new failures that arise.
Summarize the root cause, the fix, and how you used the AI assistant effectively. Highlight any trade-offs made and lessons learned for future debugging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.