The bug itself wasn't hard to spot once I actually read the tests.
First, clarify the function's contract and edge cases (e.g., empty list, null inputs). Then, walk through the buggy code to identify logical errors, and propose a fix using a set for O(1) lookups to efficiently check for self, existing friends, and duplicates. Finally, discuss trade-offs and test with representative cases.
Pro tip: Mention that you'd add unit tests covering edge cases like empty recommendations, all duplicates, and mixed invalid entries to ensure robustness. Also, consider if the function should return a boolean or a filtered list, and confirm with the interviewer.
Ask clarifying questions about input types, expected output, and constraints. Identify edge cases such as empty list, null values, and large inputs.
Trace through the existing function with sample inputs to pinpoint where it fails to reject self, existing friends, or duplicates.
Use a set for existing friends and a set to track seen recommendations, enabling O(1) checks. Iterate through the list, rejecting invalid entries.
Write the corrected function, then test with cases like self-recommendation, existing friend, duplicate, and valid recommendations.
Consider time/space complexity, alternative approaches (e.g., sorting), and whether to return a boolean or filtered list.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.