Start by clarifying the filter's intended behavior and constraints (e.g., real-time, embedded, safety-critical). Then systematically trace the code for correctness, edge cases, and performance issues, explaining the impact of each deficiency. Prioritize bugs that could cause incorrect output or system failures.
Pro tip: Demonstrate a testing mindset: mention how you would write unit tests to expose each bug, and discuss trade-offs between fixing for correctness versus performance. This shows you think beyond just spotting errors.
Ask about the filter's purpose, input/output expectations, and constraints (e.g., memory, latency, numerical precision). This ensures you evaluate the code against the right criteria.
Check for off-by-one errors, incorrect indexing, missing boundary handling, and improper initialization. Verify the filter equation is implemented as intended.
Consider empty input, single-element input, extreme values, and non-finite numbers. Identify how the code handles these and whether it could crash or produce wrong results.
Look for unnecessary computations, poor memory access patterns, or lack of optimization (e.g., loop unrolling, SIMD). Discuss trade-offs between readability and speed.
Suggest concrete corrections and how to validate them with unit tests. Mention any refactoring for clarity or maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.