Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

Abstract

The paper appeared at the International Symposium on Software Testing and Analysis (ISSTA 2017). It is part of a project on test case prioritization, selection, and execution in Continuous Integration (CI). 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. Retecs 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 the guidance of a reward function and by observing previous CI cycles. By application on 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.