The software industry is experiencing a surge in the adoption of Continuous Integration (CI) practices, both in commercial and open-source environments. CI practices facilitate the seamless integration of code changes by employing automated building and testing processes. Some frameworks, such as Travis CI and GitHub Actions have significantly contributed to simplifying and enhancing the CI process, rendering it more accessible and efficient for development teams. Despite the availability these CI tools , developers continue to encounter difficulties in accurately flagging commits as either suitable for CI execution or as candidates for skipping especially for large projects with many dependencies. Inaccurate flagging of commits can lead to resource-intensive test and build processes, as even minor commits may inadvertently trigger the Continuous Integration process. The problem of detecting CI-skip commits, can be modeled as binary classification task where we decide to either build a commit or to skip it. This study proposes a novel solution that leverages Deep Reinforcement Learning techniques to construct an optimal Decision Tree classifier that addresses the imbalanced nature of the data. We evaluate our solution by running a within and a cross project validation benchmark on diverse range of Open-Source projects hosted on GitHub which showcased superior results when compared with existing state-of-the-art methods.

该研究提出了一种利用深度强化学习技术构建最优决策树分类器的新型解决方案，以解决数据不平衡的问题，并通过对托管在GitHub上的多样化的开源项目进行内部和跨项目验证基准测试来展示了与现有最先进方法相比具有较好结果。

基于强化学习的连续集成跳过检测方法