Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
This addresses the challenge of efficient testing in CI for software developers, though it appears incremental as it adapts existing RL techniques to a specific domain.
The paper tackles the problem of minimizing feedback time in Continuous Integration by introducing Retecs, a reinforcement learning method for automatic test case selection and prioritization, which was applied to three industrial case studies to demonstrate its effectiveness.
Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.