Unleashing the Power of Mutation Testing: Supercharging Software Quality and Resilien...
summary
Mutation testing intentionally mutates code to prove test suites actually catch faults, demonstrated by a simple C++ mutant example: plus→multiply that leaves a 2+2 test passing and exposes a false sense of coverage. Practical tools mentioned: Enlman (C++) with CMake/CTest/boost/GoogleTest support and selective recompilation, Mull (Java) with JUnit/Gradle/Maven and IDE plugins, and Mutmut (Python) with pytest/unittest and GitHub/GitLab CI hooks. Expect parallel runs, CI integration, line/file exclusions, and nontrivial CPU costs scaled to your test suite.
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chapters (12)
- 0:00Introduction
- 1:48Mutation testing basics
- 3:16Ineffective tests exposed
- 5:04Code quality benefits
- 7:30Pipeline integration
- 8:34C++ with Noman
- 11:00Java with PITest
- 13:10Python with Mutmut
- 14:34Summary
- 15:17Q&A CPU costs
- 16:22Q&A selection & parallel
- 18:07Q&A trade-offs
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