Start by running the failing test cases to understand the symptoms, then systematically trace the code to identify root causes. Prioritize bugs that affect the most test cases or are easiest to fix, and verify each fix with the test suite before moving on.
Pro tip: Keep a mental (or written) list of bugs and their fixes, and after each fix, re-run all tests to catch regressions. This shows disciplined debugging and ensures you don't introduce new issues.
Read the problem statement and examine the failing test cases to identify expected vs. actual outputs. This helps narrow down where the bugs might be.
Walk through the code logic, using print statements or a debugger to pinpoint where the behavior diverges from expectations. Look for common issues like off-by-one errors, incorrect conditionals, or wrong variable usage.
Fix the most impactful bugs first—those causing multiple test failures or blocking other fixes. Make minimal changes to avoid introducing new bugs.
After each fix, run the test suite to confirm the fix and check for regressions. Repeat until all tests pass or time runs out.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, restate the problem in your own words to ensure you understand the range-based aggregation requirement. Then, identify that the underlying pattern is prefix sums by recognizing that each query asks for a sum over a contiguous subarray. Finally, explain how to precompute prefix sums to answer each query in O(1) time after O(n) preprocessing.
Pro tip: Before coding, clarify with the interviewer whether the array is static or dynamic; if dynamic, a Fenwick tree or segment tree might be needed. Also, mention edge cases like empty ranges or large sums to show thoroughness.
Ask questions to confirm the input format, query types, constraints, and whether updates are allowed. This ensures you address the correct problem.
Recognize that range sum queries can be answered efficiently using prefix sums. Explain that prefix[i] = sum of elements from index 0 to i-1.
Precompute the prefix sum array in O(n) time. For each query [L, R], compute sum = prefix[R+1] - prefix[L] in O(1) time.
State that preprocessing takes O(n) time and O(n) space, and each query takes O(1) time. Compare with naive O(n) per query approach.
Consider empty ranges, large sums (use appropriate data types), and verify with small examples. Walk through a sample query to demonstrate correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.