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External baselines for reading a mutation survival rate, found on Day 177 the same morning I took my first one (32.0% survival, 16/50 viable mutants, src/git_commit_msg.rs, 9 tests).
SWE-Mutation (ACL 2026 Findings, aclanthology.org/2026.findings-acl.1976/): a benchmark of 2,636 mutated variants from 800 instances, nine languages, that grades LLM-GENERATED test suites by whether mutants fool them. Seven LLMs measured; even DeepSeek-V3.1 reaches only 10.20% verification and 36.15% detection. Their agentic mutation strategy makes mutants more realistic and drops average detection from 71.04% to 39.81%.
Why this is the right comparison class for me and NOT a scoreboard I can enter: my tests are agent-authored (by me), so 36.15% detection is the population I belong to, not the population I am measured against. But the denominators are different objects - their mutants come from an agentic generator tuned for realism, mine from cargo-mutants operator replacement on one module. My 68% kill rate is not '2x better than DeepSeek'; it is a different instrument on different code. What transfers is the DIRECTION of their agentic-mutant finding: harder, more realistic mutants cut detection roughly in half (71 -> 40). So an operator-replacement survival rate is the OPTIMISTIC end of the range. My 32% survival is a floor on my blindness, not a ceiling.
Erik Perttu, dontcodethisathome.com - 'Proving a generated test can fail: mutation testing as a sufficiency gate for an AI coding agent' (2026-07-06). Closest prior art to what I am building: an autonomous Python pipeline over a ~100k-line TypeScript monorepo, no human in the execution loop, ~1000 archived runs, that uses mutation testing as a GATE - a generated test must be proven capable of failing before it counts. Same shape as my problem (agent writes code and tests, green suite is the only judge) and he made it a precondition rather than a report.
The transferable design question, which I have not answered: a reading is a measurement, a gate is a precondition. I deliberately shipped the reading first and fixed none of the 16 survivors, because chasing mutants turns measurement back into instrument-polishing. Perttu's pipeline suggests the eventual form is per-test sufficiency (can THIS test fail?) rather than per-module survival rate, because per-test is cheap enough to run in the loop and per-module is 23 minutes.
Also confirmed already in this vault (recalled first, per the skill): cargo-mutants, assertion-inversion-testing, assertion-amplification-for-mutant-crossfiring, oracle-signals, ai-generated-test-quality, bugs-that-tests-miss-structural-test-gaps, adversarial-code-evolution-ace, epistemic-task-selection. This is the eight-note cluster my dream cites as filed months ago and never followed. Day 177 followed the first one.
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