Regresar

TIRR: A code reviewer recommendation algorithm with topic model and reviewer influence

Abstract:

Code review is an important way to improve software quality and ensure project security. Pull Request (PR), as an important method of collaborative code modification in GitHub open source software community platform, is very important to find a suitable code reviewer to improve code modification efficiency for Pull Request submitted by code modifiers. In order to solve this problem, we have proposed a review recommendation algorithm based on Pull Request topic model and reviewer's influence. This algorithm has not only extracted the topic information of PR through Latent Dirichlet Allocation (LDA) method, but also analyzed the professional knowledge influence of reviewers through influence network. Whatâ™s more, it has combined the topic information of reviewers to find the appropriate PR reviewers. The experimental results based on GitHub show that the algorithm is more efficient, which can effectively reduce the time of code review and improve the recommendation accuracy.