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mutation-testing

Guides mutation testing workflows to evaluate and improve test suite quality.

Updated Mar 30, 2026
Search Tags:#code coverage#test quality#mutation testing#stryker#mutmut#pitest#ci integration
One-click install
npx skills add https://github.com/rubrical-works/idpf-praxis-skills --skill mutation-testing-rubrical-works
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mutation-testing
Source: https://github.com/rubrical-works/idpf-praxis-skills/tree/main/Skills/mutation-testing
Command: npx skills add https://github.com/rubrical-works/idpf-praxis-skills --skill mutation-testing-rubrical-works

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Code coverage alone cannot reveal whether tests actually verify behavior; this Skill helps developers assess test suite effectiveness by introducing small code mutations and checking whether tests detect them. ## Core Features & Use Cases - Mutation Operator Reference: Detailed tables of arithmetic, relational, logical, constant, and statement mutation operators across Python, JavaScript, Java, C#, and Ruby. - Score Interpretation: Benchmarks and strategies for understanding mutation scores, analyzing survived mutants, and handling equivalent mutants. - Framework Setup Guides: Configuration and CI integration examples for mutmut, Stryker, PIT/Pitest, Stryker.NET, and mutant. - Use Case: A team with 95% code coverage suspects weak assertions. They run Stryker, find a 68% mutation score, review survived mutants, and add boundary and error-path tests to strengthen the suite. ## Quick Start Ask the AI to set up mutation testing for your project and analyze the survived mutants to improve your test suite.

Frequently Asked Questions about mutation-testing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up mutation testing for my project?▼

Install a mutation testing tool for your language: mutmut for Python, Stryker for JavaScript/TypeScript, PIT for Java, Stryker.NET for C#, or mutant for Ruby. Configure the target source and test directories, then run the tool to generate a mutation score report.

What is a good mutation testing score?▼

A score of 90-100% indicates an excellent test suite, 75-89% is good, and below 60% signals a weak suite needing attention. Critical code like authentication and payment processing should target 90%+, while low-risk code like logging can accept 60%+.

Stryker vs mutmut vs PIT: which mutation testing tool should I use?▼

Choose based on your language: Stryker for JavaScript/TypeScript offers excellent reporting, mutmut provides a simple CLI for Python, and PIT is the industry standard for Java. Stryker.NET covers C# and mutant covers Ruby.

Why do some mutants survive even with good tests?▼

Survived mutants typically indicate missing boundary tests, weak assertions that don't check actual values, or missing error case tests. Some are equivalent mutants that behave identically to the original code and cannot be killed.

How do I handle equivalent mutants in mutation testing?▼

Equivalent mutants produce identical behavior to the original code, so no test can kill them. Review them manually, mark them as equivalent in your tool configuration, and exclude them from the score calculation to avoid misleading results.

Can mutation testing run in CI pipelines?▼

Yes, run mutation testing on pull requests with limited scope to changed files, and schedule full runs nightly or before releases. Configure score thresholds to fail builds on regression, and use parallel execution to manage runtime.

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