Start by restating the problem and clarifying constraints (input size, edge cases, expected complexity). Then outline a brute-force solution, identify its inefficiencies, and optimize using appropriate data structures or algorithms. Finally, walk through your code with a test case and analyze time/space complexity.
Pro tip: Before coding, write down 2-3 edge cases (empty input, single element, large values) and mentally test your algorithm against them. This shows thoroughness and often catches bugs early.
Restate the problem in your own words and ask clarifying questions about input format, constraints, and edge cases. Confirm expected output and any special conditions.
Discuss a brute-force solution first, then analyze its time/space complexity. Propose optimizations using appropriate data structures (e.g., hash maps, heaps) or algorithmic techniques (e.g., two pointers, dynamic programming).
Outline the chosen algorithm step-by-step, then write clean, modular code. Use meaningful variable names and handle edge cases explicitly.
Walk through your code with a simple example and the edge cases you identified. If time permits, discuss potential bugs and how you would fix them.
State the time and space complexity of your solution, and compare it to the brute-force approach. Mention any trade-offs made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Only got 1 out of 6 test cases through and I still don't fully understand why.
First, read the buggy code carefully to understand its intended behavior and identify any obvious issues. Then, use AI assistance to systematically analyze the code, but verify each suggested fix by reasoning through edge cases and testing. Finally, implement and validate the corrected code, ensuring it meets the original requirements.
Pro tip: Treat AI as a collaborative tool, not an oracle: always question its suggestions and validate fixes with your own test cases. This demonstrates critical thinking and reduces the risk of introducing new bugs.
Read the problem statement and the buggy code to grasp what the code is supposed to do and identify any obvious errors or inconsistencies.
Use AI to quickly scan the code for potential bugs, such as off-by-one errors, incorrect conditionals, or missing edge cases, and to suggest possible fixes.
Critically evaluate each AI suggestion by tracing through the code with sample inputs, checking edge cases, and ensuring the fix aligns with the intended logic.
Apply the validated fixes, then run the code against a variety of test cases, including edge cases, to confirm correctness and no regressions.
Clearly articulate the bugs found, how AI assisted, and why the fixes are correct, demonstrating a systematic debugging approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.