Rethinking Software Quality Metrics in the Age of AI-Driven Development

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AI is rapidly taking over many software development tasks, prompting engineers and quality teams to reconsider how software quality should be measured going forward. A software quality mentor argues that metrics must be tailored to team context and business needs rather than applied universally. He divides quality metrics into two groups: those for stakeholders and those for development teams, emphasizing that bug counts alone fail to reflect true product health. Key recommended metrics include Mean Time to Resolve (MTTR) production issues and automated test coverage — but only when analyzed together to ensure tests address critical scenarios. DORA Metrics are also highlighted as a reliable indicator of overall process quality, linking fast, low-incident delivery to well-built testing and observability pipelines.

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