← American Express Interview Insights
The tricky part isn't writing code, it's resisting the urge to rewrite things.
Start by reading the failing test output carefully to understand the expected vs. actual behavior and trace it back to the specific function. Then explain the root cause in plain terms before proposing a minimal code change that fixes the bug without altering unrelated logic.
Pro tip: Before fixing, state your hypothesis and how you'd verify it (e.g., add a print or run a focused test), showing a systematic debugging mindset rather than guessing. Also, mention that you'd run the full test suite after the fix to ensure no regressions.
Identify which test(s) failed, the expected result, and the actual result. Note any error messages or stack traces that point to the relevant function.
Locate the function under test and follow the execution path to find where the actual behavior diverges from the expected. Use the test input to reason through the logic.
Articulate why the bug occurs—e.g., off-by-one error, incorrect variable, missing edge case—and how it leads to the test failure.
Suggest the smallest code change that corrects the bug, such as adjusting a condition, changing an operator, or fixing a return value, without refactoring unrelated code.
Describe how you would test the fix: rerun the failing test, then the full suite, and consider adding a new test case if the bug was uncovered by an edge case.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt more comfortable here than in part one.
Start by carefully reading the spec and the failing tests to understand the exact expected behavior, edge cases, and constraints. Then, design a clean algorithm that satisfies the spec, implement it incrementally, and run the tests frequently to validate your solution. Finally, review for efficiency and clarity, and be prepared to explain your trade-offs.
Pro tip: Before writing any code, restate the problem in your own words and confirm your understanding with the interviewer. This shows strong communication skills and prevents you from solving the wrong problem.
Read the spec and failing tests carefully. Identify inputs, outputs, edge cases, and any constraints. Ask clarifying questions if needed.
Choose an appropriate data structure and algorithm. Consider time and space complexity, and discuss trade-offs with the interviewer.
Write the function step by step, testing small parts as you go. Use the failing tests to guide your implementation.
Run the provided tests and any additional edge cases you can think of. Fix any issues and ensure all tests pass.
Refactor for readability and efficiency. Explain your solution and any trade-offs you made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.