A Time Window based Reinforcement Learning Reward for Test Case Prioritization in Continuous Integration

Continuous integration refers to the practice of merging the working copies of all developers into the mainline frequently. Regression testing for each mergence is characterized by continually changing test suite, limited execution time, and fast feedback, which demands new test optimization techniques. Reinforcement learning is introduced for test case prioritization to save computing resources in continuous integration environment, where a reasonable reward function is highly important for learning strategy, since the process of reinforcement learning is a reward-guided behavior. In this paper, APHFW, a novel reward function is proposed by using partial historical information of test cases effectively for fast feedback and cost reduction. The experiments are based on three open-source data sets, and the results show that the proposed reward function is more cost-effect than other reinforcement learning rewards in continuous integration environment.