'mutation-testing' 검색 결과

Evaluate whether tests catch defects in business logic, calculations, or state machines. Using Stryker, the skill introduces small code changes, reviews which changes escape detection, and helps strengthen tests where important behavior is insufficiently checked.

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• Checking test quality beyond code coverage
• Introducing Stryker for a single module
• Strengthening tests for critical calculations

Audit a Python pytest suite for missed faults, uncovered code, slow tests, and unstable results. Deeper checks deliberately alter code to measure detection, while proposed test removals receive separate validation. The skill reports findings without changing tests.

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• Find gaps in a pytest suite's fault detection
• Investigate slow tests and setup costs
• Identify intermittent or order-dependent failures

Check whether tests detect faults in changed Python code by deliberately modifying the changed lines. Add tests for missed behavior, confirm which tests fail against the previous code, and report measured evidence alongside any remaining gaps.

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• Verify test coverage for a Python feature or fix
• Add tests for faults the current suite misses
• Confirm new tests fail against the previous code

Assess test strength by introducing deliberate code changes with mewt or muton. Configure test runs, examine changes that tests miss, distinguish harmless equivalents from testing gaps, and investigate potential bugs in the affected code.

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• Set up and scope a mutation testing run
• Investigate code changes that tests fail to detect
• Distinguish equivalent changes from testing gaps

Writes, reviews, and debugs property-based tests, which generate inputs to check general rules. Helps test conversions, parsers, normalization, and state invariants, assess whether existing assertions are meaningful, and distinguish faulty rules from implementation bugs.

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• Testing encode/decode round trips across generated inputs
• Checking normalization and sorting properties
• Reviewing whether existing property tests assert meaningful rules

Add and review preconditions on operations that delete, overwrite, rewrite history, or resolve a caller-supplied name to a filesystem path — refusing instead of warning, placing the guard ahead of the first mutation, structural classification rather than string-prefix matching, name validation plus resolved-path containment as two independent checks, why a guard on the destructive path is invisible to the dry-run, and mutation-testing each half separately. Use when writing or reviewing a `--rebuild`/`--reset`/`--purge` command, a bulk delete, a history rewrite, an `rm -rf`-shaped step, or any code that turns an argument into a file path.

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EXPERIMENTAL — mutation testing with muter to measure whether tests actually assert anything: mutants that survive reveal assertion-free coverage. Advisory report only, never a merge gate. Use on engine/logic targets when coverage numbers look good but bugs still slip through.

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Mutation testing ile test suite kalitesini olc. Stryker, mutmut, go-mutesting destegi.

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Validate test effectiveness with mutation testing using Stryker (TypeScript/JavaScript with Vitest or bun test via @hughescr/stryker-bun-runner) and mutmut (Python). Find weak tests that pass despite code mutations. Use to improve test quality.

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Expert guide for property-based testing (fast-check, Hypothesis) and mutation testing (Stryker, mutmut) — AI-generated test properties, invariant discovery, mutation score optimization, and test suite quality verification / Panduan ahli property-based testing (fast-check, Hypothesis) dan mutation testing (Stryker, mutmut) — properti tes yang dihasilkan AI, penemuan invarian, optimasi skor mutasi, dan verifikasi kualitas suite tes.

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Mutation testing patterns for verifying test effectiveness. Use when analyzing branch code to find weak or missing tests.

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Turn surviving mutants into better tests or bug reports: on a survivor, a red MSI gate, or before a threshold drops.

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Spec-locked TDD with a mutation-testing gate — refine the intent into an immutable Gherkin contract (SHA-256 frozen), implement under the three laws of TDD, pass a judge audit, and merge only if every mutant dies.

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Refute-before-prove research discipline: automated counterexample sweeps before any formalization, meter-integrity audits for information-bound experiments, mutation-tested model checkers, pre-registered commit-or-cut gates for speculative directions, wrong-turn reporting, and numeric-claim provenance. Use when validating any new theorem, bound, equilibrium, or security claim; when building or trusting a checker, simulator, or experiment; or when deciding whether a speculative research direction lives or dies. NOT for exploratory brainstorming, product prioritization, writing style, or literature review.

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