In this episode, Lucas and Luna dive into test prioritization — a smarter way to run test suites that cuts CI pipeline time without sacrificing quality. Lucas explains how techniques like risk-based sorting, historical failure data, and machine learning models can reduce test execution by 40 percent or more. They discuss real-world examples from Google's test automation research and how teams at companies like Spotify have implemented prioritization to speed up deployments. Luna asks about the trade-offs: does prioritization miss bugs that a full run would catch? Lucas walks through strategies like time-bucketed runs and smoke test layers to mitigate risk. If you've ever waited an hour for a CI pipeline to finish, this episode will give you concrete tactics to speed things up.

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