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Debugging Testing and Optimization Questions

Core engineering skills for identifying, diagnosing, testing, and improving code correctness and performance. Covers approaches to finding and fixing bugs including reproducible test case construction, logging, interactive debugging, step through debugging, and root cause analysis. Includes testing strategies such as unit testing, integration testing, regression testing, test driven development, and designing tests for edge cases, boundary conditions, and negative scenarios. Describes performance optimization techniques including algorithmic improvements, data structure selection, reducing time and space complexity, memoization, avoiding unnecessary work, and parallelism considerations. Also covers measurement and verification methods such as benchmarking, profiling, complexity analysis, and trade off evaluation to ensure optimizations preserve correctness and maintainability.

MediumTechnical
0 practiced
Design an experiment to prove that an optimization preserves correctness. Pick a function with numerical outputs (e.g., floating point), describe tests to compare outputs, tolerance thresholds, and strategies to detect regressions introduced by optimizations like vectorization or parallelism.
MediumTechnical
0 practiced
Write a small C function that leaks memory when called repeatedly, then explain how you would detect and fix the leak using valgrind and heap analysis. Provide code snippet and high-level commands you would run.
HardTechnical
0 practiced
Write a short Java method that demonstrates a deadlock between two threads using synchronized blocks. Then propose two code-level fixes and describe runtime checks or tests to detect deadlocks in CI.
EasyTechnical
0 practiced
You find a failing function that behaves incorrectly only for a specific input. Outline how you would create a minimal, reproducible test case that isolates the bug. Include steps you would take to reduce external dependencies and ensure the test can run in CI.
HardTechnical
0 practiced
Design a small experiment to compare the energy consumption (power usage) of two implementations of a tight computation loop (e.g., image filter) and explain why energy might differ even with similar CPU usage. Mention measurement tools and controlled variables.

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