The actual bug was in printMaze, where empty cells were being printed with the wrong symbol instead of an asterisk.
Start by understanding the expected behavior and reproducing the bug with a minimal test case. Then systematically trace the code, using debugging tools and print statements to isolate the root cause, and finally fix it while considering edge cases and trade-offs.
Pro tip: Narrate your thought process clearly, including hypotheses you discard, to demonstrate structured problem-solving. Also, after fixing, suggest adding a regression test to prevent future occurrences.
Read the codebase to grasp the maze solver's intended functionality and identify the input/output contract. Clarify any ambiguities about the expected behavior before diving into debugging.
Create a minimal test case that triggers the bug, ensuring you can consistently observe the incorrect behavior. This helps confirm the issue and provides a baseline for verification.
Use debugging techniques such as print statements, breakpoints, or binary search through the code to narrow down where the logic deviates from expectations. Form and test hypotheses about potential causes.
Apply a targeted fix that addresses the root cause without introducing new issues. Re-run the test case and additional edge cases to ensure the fix works and doesn't break other functionality.
Discuss potential improvements such as adding regression tests, refactoring for clarity, or considering alternative approaches. Highlight any trade-offs made during the fix.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I wrote comments that were basically just restating what the code did, which is useless.
Treat the debugging process as a narrative: first restate the problem and form a hypothesis, then instrument the code with comments that explain what you're checking and why, and finally validate or reject the hypothesis based on evidence. Use comments to make your reasoning transparent to the interviewer, not just to document the code.
Pro tip: Narrate your thought process out loud while writing comments, and explicitly call out when you're changing your hypothesis based on new evidence—this shows scientific rigor and adaptability, which Meta values.
Ask clarifying questions to understand the expected behavior and reproduce the bug consistently. Comment the reproduction steps and any assumptions you're making.
Based on the symptoms, propose one or more likely root causes. Write a comment stating your hypothesis and what evidence would confirm or refute it.
Add strategic logging or assertions with comments explaining what each check is testing. Narrow down the failure to a specific function or line.
Once the root cause is identified, comment the fix and why it addresses the issue. Verify the fix resolves the bug without introducing regressions.
Comment on how the bug could have been prevented (e.g., better tests, code review) and what you learned. This shows ownership and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the most interesting part of the whole thing.
Start by emphasizing the importance of understanding the test's intent and the code's expected behavior before making any changes. Then outline a systematic process: investigate the failure, determine if the test is valid, and decide whether to fix the code or flag the test. Highlight communication and documentation as key to maintaining team alignment.
Pro tip: Frame the decision as a trade-off between short-term velocity and long-term code health, and mention that you'd loop in the test author or domain expert when uncertain. This shows you value collaboration and sustainable engineering practices.
Run the failing test locally and examine the error message, stack trace, and test code to understand what it's checking and why it fails.
Determine if the test is correctly written and reflects a valid requirement. Check if it's malformed (e.g., syntax errors, incorrect assumptions) or if it's testing outdated behavior.
If the test is valid, investigate whether the code has a bug or if the test reveals a legitimate issue. Consider the impact of fixing the code versus the risk of ignoring a real problem.
If the test is malformed, flag it and propose removal or correction with justification. If the test is valid, fix the code. Document your reasoning and communicate with the team.
Suggest improvements to test review processes or add safeguards to catch malformed tests early, such as linting or peer reviews.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.