Test Case Selection in Continuous Integration Using Reinforcement Learning with Linear Function Approximator

Abstract: Continuous Integration (CI) has become an essential practice in software development, allowing teams to integrate code changes frequently and detect issues early. However, the selection of proper test cases for CI remains a challenge, as it requires balancing the need for thorough testing with the minimization of execution time and resources. This study proposes a practical and lightweight approach that leverages Reinforcement Learning with a linear function approximator for test case selection in CI. Several models are created where each one focuses on a different feature set. The proposed method aims to optimize the selection of test cases by learning from past CI outcomes, both the historical data of the test cases and the coverage data of the source code, and dynamically adapting the models for encountering new test cases and modified source code. Through experimentation and comparison between the models, the study demonstrates which feature set is optimal and efficient. The result indicates that Reinforcement Learning with a linear function approximator using coverage information can effectively assist in selecting test cases in CI, leading to enhanced software quality and development efficiency.