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Introduction Test automation is often introduced with one promise: speed . Faster feedback. Faster releases. Faster confidence. And in the early stages, automation usually delivers on that promise. Test execution times drop, manual effort reduces, and pipelines look healthier. But over time, many teams experience a paradox : as test automation grows, delivery slows down. Pipelines become fragile. Failures become noisy. Maintenance work increases. Teams spend more time fixing

Introduction Metrics are everywhere in software quality assurance. Test coverage, pass rates, defect counts, execution time — dashboards are full of numbers meant to reassure teams that quality is under control. And for a while, they help. But at a certain point, QA metrics stop improving decision-making. They continue to report activity, yet add little clarity about risk, readiness, or real-world behavior. When that happens, metrics don’t just lose value — they can actively

Introduction Test coverage is one of the most commonly cited indicators of software quality. Teams track it, report it, and sometimes celebrate it. A high percentage can feel reassuring — a signal that the system has been “thoroughly tested.” But coverage is often misunderstood. While it measures where tests have executed , it says very little about what risks have actually been addressed . In practice, high coverage can coexist with fragile systems, late-stage surprises, and