LDA Topic Model-Based Recommendation Algorithm for GitHub Actions Workflow Projects
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 ofopen-source software repositories.
万方数据
维普
