Lucas and Luna explore mutation testing, a technique that goes beyond code coverage by introducing small changes—mutations—into your source code to see if your test suite catches them. The episode drills into a real-world case: a UK fintech called Kaluza that used mutation testing to find a critical bug in its energy-balancing algorithm, despite 95 percent line coverage. Lucas explains why coverage metrics often hide weak tests, how mutation operators work, and why the approach is gaining traction in 2026 as CI pipelines get faster. Luna pushes back on the computational cost argument and shares a practical example from a friend's startup that slashed test suite size while increasing defect detection. The episode closes with a forward-looking question about whether AI-generated mutations will become standard.

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