Edge Case Identification and Testing Questions

Focuses on systematically finding, reasoning about, and testing edge and corner cases to ensure the correctness and robustness of algorithms and code. Candidates should demonstrate how they clarify ambiguous requirements, enumerate problematic inputs such as empty or null values, single element and duplicate scenarios, negative and out of range values, off by one and boundary conditions, integer overflow and underflow, and very large inputs and scaling limits. Emphasize test driven thinking by mentally testing examples while coding, writing two to three concrete test cases before or after implementation, and creating unit and integration tests that exercise boundary conditions. Cover advanced test approaches when relevant such as property based testing and fuzz testing, techniques for reproducing and debugging edge case failures, and how optimizations or algorithmic changes preserve correctness. Interviewers look for a structured method to enumerate cases, prioritize based on likelihood and severity, and clearly communicate assumptions and test coverage.

EasyTechnical

93 practiced

You have a Python function signature:

python

def safe_mean(numbers: List[float]) -> Optional[float]:
    """Return the mean of numeric values. Ignore NaNs. Return None if no finite values."""

Write three pytest unit tests that exercise realistic edge cases: empty list, list containing NaN and infinite values, very large values that could cause overflow if cast incorrectly, and a single-element list. Provide the pytest test code snippets (inputs and expected asserts).

HardTechnical

86 practiced

You need to ensure that grouped results sorted by timestamp preserve a stable tie-breaker so downstream consumers see deterministic ordering. Describe properties you would test with a property-based testing framework: idempotence of sorting, stability on repeated runs, and invariants when timestamps are equal. Show one or two property ideas in plain pseudocode that would expose non-deterministic ordering bugs.

HardTechnical

71 practiced

Explain how integer overflow can occur in a numpy int32 aggregation when summing billions of integers (wraparound), and write a unit test that detects the wraparound behavior. Propose a code fix and a regression test to ensure the fix (for example, using int64 or Python int) prevents wraparound while keeping performance acceptable.

MediumTechnical

99 practiced

You're summing very large counts using numpy int32 and see wraparound in production. Design unit and property-based tests to detect integer overflow and floating-point underflow/overflow in aggregations. Provide concrete inputs that would trigger the problem and describe programmatic assertions (for example checking for negative sums when domain forbids them). Suggest fixes and how to test that the chosen fix prevents the issue without performance regression.

MediumTechnical

73 practiced

You maintain an anomaly detector that flags values with z-score > 3. Create test cases that highlight edge behavior when sample size is small or equal to 1, when variance is zero, and when there is a single extreme outlier. Provide explicit numeric examples (inputs and which indices should be flagged) and explain false-positive/false-negative tradeoffs for small N.

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