Code Quality and Debugging Practices Questions

Focuses on writing maintainable, readable, and robust code together with practical debugging approaches. Candidates should demonstrate principles of clean code such as meaningful naming, clear function and module boundaries, avoidance of magic numbers, single responsibility and separation of concerns, and sensible organization and commenting. Include practices for catching and preventing bugs: mental and unit testing of edge cases, assertions and input validation, structured error handling, logging for observability, and use of static analysis and linters. Describe debugging workflows for finding and fixing defects in your own code including reproducing failures, minimizing test cases, bisecting changes, using tests and instrumentation, and collaborating with peers through code reviews and pair debugging. Emphasize refactoring, test driven development, and continuous improvements that reduce defect surface and make future debugging easier.

EasyTechnical

36 practiced

Describe how code reviews and pair debugging contribute to reducing defect surface. Provide concrete guidelines you would enforce in reviews to improve debug-ability of code.

MediumTechnical

38 practiced

Describe a practical workflow you would follow to reproduce and debug a flaky test in the CI pipeline that fails intermittently. Include steps from reproducing locally to root-cause analysis and permanent fix.

HardTechnical

40 practiced

Design a small set of linters/custom static checks to detect common anti-patterns that make debugging harder (e.g., swallowing exceptions, broad try/catch, empty catch blocks). For each check, explain how you'd implement it and a sample warning message.

EasyTechnical

42 practiced

Explain the key principles of clean code you follow when writing production software. Give concrete examples (in any language you choose) showing: meaningful naming, function length and single responsibility, avoidance of magic numbers, and sensible module boundaries.

Sample Answer

I follow these clean-code principles with concrete examples:1) Meaningful naming: choose intent-revealing, pronounceable names. Example (Python):

python

# bad
def fn(a):
    return a*9.81
# good
def convert_mass_to_weight_kg(mass_kg):
    GRAVITY_M_S2 = 9.81
    return mass_kg * GRAVITY_M_S2

2) Function length & single responsibility: functions do one thing and fit on screen. Split complex logic into helpers with clear names.3) Avoid magic numbers: use named constants (GRAVITY_M_S2 above) and config files for env-specific values.4) Sensible module boundaries: group by feature or domain (e.g., auth/, payments/, orders/) and expose a small API. Each module has clear public functions and hides internals.Reasoning: these practices improve readability, reduce cognitive load, and make testing and maintenance easier.

HardTechnical

49 practiced

Describe a debugging workflow for an unknown defect that only appears under heavy load in production. Include steps for gathering data with minimal customer impact, local reproduction strategies, and how you'd use feature flags or traffic shaping to test fixes.

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