基于LDA主题模型的GitHub Actions工作流项目推荐算法

LDA Topic Model-Based Recommendation Algorithm for GitHub Actions Workflow Projects

在CI/CD实践中,自动化已成为软件开发实践中的一种规范。GitHub引入GitHub Actions为软件维护者提供自动化的持续集成工作流方案,尽管其为开发者提供了诸多便利,GitHub社区也提供了许多第三方的GitHub Actions服务,但仍然只有极少的项目在使用。为了满足开发人员对工作流自动化的需求,减少非开发任务工作量,提出一种基于隐含狄利克雷分布(LDA)主题模型和Jensen-Shannon距离的GitHub Actions工作流项目推荐算法。通过对GitHub Actions存储库的README文件进行主题建模,将GitHub的事件描述文本和用户输入作为模型输入,为正在开发的代码仓库推荐GitHub Actions服务。将该推荐模型与标准的基于余弦相似度的方法进行比较的实验结果表明,该方法能有效改善开源软件仓库的推荐精度。

In the practice of CI/CD,automation has become a norm in software development.GitHub introduces GitHub Actions to provide software maintainers with an automated,continuous integration workflow solution.Despite providing developers with many conveniences and the GitHub community offering many third-party GitHub Actions services,only a few projects are still in use.In order to meet the needs of de⁃velopers for workflow automation and reduce non development task workload,a GitHub Actions workflow project recommendation algorithm based on implicit Dirichlet distribution(LDA)topic model and Jensen-Shannon distance is proposed.By theme modeling the README file of the GitHub Actions repository,the event description text and user input of GitHub are used as model inputs to recommend GitHub Actions ser⁃vices for the code repository under development.The experimental results comparing the recommendation model with the standard cosine simi⁃larity based method show that this method can effectively improve the recommendation accuracy of open-source software repositories.