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Blanked for a second because it sounds trivial until you actually think about it.
Start by clarifying the function's contract: it should return heads or tails with equal probability and be random. Then outline a test strategy that includes deterministic tests (e.g., mocking randomness) and statistical tests (e.g., chi-square) to verify fairness, while discussing trade-offs like flakiness and sample size.
Pro tip: Mention that statistical tests can be flaky, so you'd set a high confidence level and possibly run them as separate, non-blocking tests. Also, consider seeding the random number generator for reproducibility in deterministic tests.
Confirm the expected behavior: the function returns 'heads' or 'tails' with equal probability, and uses a random source. Ask if the randomness source can be injected for testability.
Mock the random number generator to return fixed values and assert the function maps them correctly to 'heads' or 'tails'. This tests the logic without randomness.
Run the function many times (e.g., 10,000) and use a chi-square goodness-of-fit test to check if the distribution is approximately 50/50. Set a significance level (e.g., 0.01) to avoid flakiness.
Test that the function handles edge cases, such as when the random source returns boundary values (0 or 1). If the function can fail, test error conditions.
Acknowledge that statistical tests are probabilistic and may occasionally fail; suggest running them less frequently or with a very low false-positive rate. Also, consider performance if the function is called frequently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.