Judge whether coverage is meaningful
You asked an AI agent to 'add tests to get the `PricingEngine` module to 90% coverage.' It delivered: coverage jumped to 91%, all green. The PR looks like solid test work. Before you trust that this module is now well-tested, how do you judge whether these tests are meaningful or just coverage theater?
Implement
surviving_mutants(mutant_ids: list[str], kill_lines: list[str]) → list[str]Examples
in
[["m_lt_to_lte","m_round_half_down","m_discount_skip"],["test_price_basic>m_lt_to_lte","test_zero_quantity>","test_snapshot_output>"]]out["m_round_half_down","m_discount_skip"]in
[["m_lt_to_lte","m_round_half_down"],["test_boundary_price>m_lt_to_lte,m_round_half_down","test_smoke>"]]out[]in
[["m_currency_floor"],["test_calls_engine>m_ghost_mutant","test_no_assertions>"]]out["m_currency_floor"]What a strong answer looks like
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it (tests, edge cases, reading critically), and how you’d re-prompt or decompose to get it right.
0:00 of about 20 min
Vibe & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.