The prompt hands you three bugs on a plate, which feels generous until you realize the test suite has opinions about things the prompt never mentions.
Start by reproducing the bugs through the failing tests to understand the expected behavior, then systematically trace each issue to its root cause using the AI assistant for code exploration and hypothesis testing. Fix the bugs one at a time, verifying with tests after each change, and ensure the fixes are minimal and don't introduce regressions.
Pro tip: Demonstrate that you use the AI assistant as a tool for accelerated debugging, not as a crutch: articulate your own reasoning and validate AI suggestions with tests and code review. This shows you can leverage AI while maintaining engineering rigor.
Run the test suite to see which tests fail and read the error messages and stack traces to understand the expected vs. actual behavior for each bug.
For each bug, trace the code path from the failing test to identify the faulty logic, using the AI assistant to explain code, suggest potential causes, and locate relevant files.
Make targeted code changes to address each root cause, then run the specific test to confirm the fix and ensure no other tests break.
After all fixes, run the entire test suite to ensure all tests pass, and review changes for code quality, edge cases, and potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.