What is a good mutation score?

A score of 90% or higher is considered excellent. Scores between 75-89% are good but have gaps, while scores below 50% indicate that tests need significant improvement.

What is mutation testing?

Mutation testing involves modifying code in small ways (mutants) to see if existing tests fail; if tests pass, the mutant 'survived,' indicating a gap in your test suite's effectiveness.

Which frameworks does this skill support?

The skill currently supports Stryker for JavaScript and TypeScript, PIT for Java, and mutmut for Python.

Will mutation testing slow down my development workflow?

Mutation testing is compute-intensive. This skill provides guidance on running it against critical modules only or using incremental modes to maintain performance.

Can I use this on a new project without tests?

No, this skill is designed for projects with an existing test suite. It is most effective when you already have at least 50% code coverage.

Mutation Testing Analysis

Name: Mutation Testing Analysis
Author: cskiro

bycskiro

Security & Testing

Evaluates and improves test suite effectiveness by introducing deliberate code changes to identify weak tests and unverified logic.

About

This skill empowers developers to move beyond superficial code coverage metrics by implementing mutation testing frameworks like Stryker, PIT, and mutmut. It proactively analyzes codebases to identify mutation targets, generates deliberate code changes (mutants), and verifies whether existing test suites catch these intentional bugs. By reporting mutation scores and identifying 'surviving' mutants, the skill provides actionable insights to strengthen test suites, ensuring they are capable of catching real-world regressions before they reach production. It is an essential tool for teams prioritizing high-reliability software and rigorous quality assurance.

Key Features

Guidance on performance optimization for compute-intensive test runs
Strategic recommendations for improving tests based on specific failures
Multi-language support for Stryker (JS/TS), PIT (Java), and mutmut (Python)
Comprehensive reporting of mutation scores and surviving mutants
0 GitHub stars
Automated identification of critical mutation targets and code paths

Use Cases

Assessing the quality of critical modules before a major production release
Validating test suite effectiveness when code coverage is high but confidence is low
Identifying 'ghost tests' that pass but fail to verify boundary conditions or logic

About

Key Features

Guidance on performance optimization for compute-intensive test runs
Strategic recommendations for improving tests based on specific failures
Multi-language support for Stryker (JS/TS), PIT (Java), and mutmut (Python)
Comprehensive reporting of mutation scores and surviving mutants
0 GitHub stars
Automated identification of critical mutation targets and code paths

Use Cases

Assessing the quality of critical modules before a major production release
Validating test suite effectiveness when code coverage is high but confidence is low
Identifying 'ghost tests' that pass but fail to verify boundary conditions or logic