Mutation testing
AssessTools
A testing technique that introduces small code changes to measure how well tests catch defects.
Why it's here
Placed in Assess: 4 article(s) of evidence from 1 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 40%.
Evidence (4)
- 5Martin Fowler·5/27/2026researchTest suite as a regression sensor for AI code
The article describes using a set of automated “sensors” to monitor maintainability in AI-generated codebases, with the test suite serving as a regression sensor. It discusses combining tests with static analysis, dependency checks, and mutation testing to catch issues early and support self-correction before changes reach humans.
- 4Martin Fowler·5/20/2026researchThree More Static Code Analysis Sensors
Martin Fowler highlights a practical follow-up on using static code analysis and related sensors to help coding agents keep codebases maintainable. The article discusses feedback tools such as linting, dependency rules, coupling analysis, modularity review, and mutation testing to catch issues early and support self-correction.
- 5Martin Fowler·5/19/2026researchMaintainability sensors for coding agents
The article describes practical experiments with using automated “sensors” to help AI coding agents keep a codebase maintainable. It highlights tools such as linting, dependency checks, test coverage, mutation testing, and structural analysis to catch maintainability issues early and support self-correction before changes reach humans.
- 5Martin Fowler·4/21/2026researchThoughtworks Radar highlights AI, security, and code review
Thoughtworks published the 34th volume of its Technology Radar, which is dominated by AI-related topics but also revisits established practices such as pair programming, mutation testing, DORA metrics, and accessibility. The piece emphasizes security risks in agentic tools, especially prompt injection and broad permission access, and argues that durable code still needs regular human review.