The setup sounds reasonable until you're actually in it.
Start by reproducing the failures with minimal test cases to understand the symptoms, then systematically narrow down the root cause using debugging tools and code inspection. Once identified, implement a targeted fix and validate it with regression tests to ensure no new issues arise.
Pro tip: Demonstrate a methodical approach by documenting each step of your investigation, including hypotheses and how you tested them. This shows maturity and makes your reasoning transparent to the interviewer.
Run the provided test suite or create minimal examples to consistently reproduce the failures. Document the exact error messages, stack traces, and conditions under which they occur.
Use debugging techniques (e.g., print statements, debugger, bisecting) to narrow down which part of the Mako engine is causing the issue. Compare with the original Mako code to spot deviations.
Analyze the isolated code to determine the underlying bug, such as a logic error, incorrect variable scope, or mishandled edge case. Explain why it causes the observed failures.
Write a minimal fix that addresses the root cause without introducing side effects. Add or update tests to cover the bug and ensure all existing tests pass.
Run the full test suite and any additional edge cases to confirm the fix. Discuss potential trade-offs of your solution and alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part that genuinely surprised me.
Start by reproducing the failure with a minimal template and enabling debug/trace logs to pinpoint where the tag is dropped. Then walk the lexer and parser flow to identify the missing registration or emission point, and patch it with a test to prevent regression.
Pro tip: Before diving into code, check if the tag is a known keyword or custom directive—often the fix is as simple as adding it to the lexer's keyword map or parser's tag registry. Also, ensure the patch handles edge cases like nested tags or attributes.
Create a minimal template that triggers the failure and confirm the error. Use debug flags or logging to see the token stream and parse tree.
Examine how the lexer tokenizes the template. Check if the tag is recognized as a valid token type; if not, identify where tag names are matched (e.g., regex, keyword list) and add the missing tag.
Follow the parser's handling of tokens. Verify if the parser expects the tag to be emitted by the lexer or registered in a tag handler map. If missing, add the tag to the appropriate registry or emission logic.
Implement the fix at the identified point, ensuring it integrates with existing tag handling. Add unit tests for the tag in isolation and integration tests with the full compiler.
Run the full test suite to ensure no regressions. Document the change and update any relevant tag lists or documentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Easier to talk through than the debugging itself.
Start by describing the regression test you would write, focusing on reproducing the exact bug scenario and asserting the correct behavior. Then analyze the root cause and identify gaps in testing, code review, or monitoring that allowed the bug to slip through. Conclude with concrete preventive measures to avoid similar issues in the future.
Pro tip: Emphasize writing a test that fails without the fix and passes with it, and mention adding a comment linking the test to the bug report for traceability. Also, discuss how you would share the root cause analysis with the team to improve processes.
Briefly restate the bug, its impact, and the fix you implemented to ensure clarity and context.
Outline a test that reproduces the bug, including setup, execution, and assertions. Mention the test level (unit, integration, etc.) and why it's appropriate.
Explain why the bug occurred and why existing tests didn't catch it. Consider factors like edge cases, test coverage, and assumptions.
Suggest improvements to testing, code review, monitoring, or development practices to catch similar bugs earlier.
Conclude with key takeaways and how you would apply these lessons to future work.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.