An Application of the PageRank Algorithm to Commit Evaluation on Git Repository
Suzuki Sho, Hirohisa Aman, Sousuke Amasaki, Tomoyuki Yokogawa, Minoru Kawahara · 2017
Many empirical studies have reported notable theories or methods for evaluating or predicting code quality through analyses of code repositories. This paper has yet another point of view: it focuses on "commits" rather than source code. That is to say, this paper proposes to evaluate commits themselves. When an aim of a commit is to fix a bug, there can be another preceding commit which made a reason of the bug fixing. Those commits are linked by a bug fixing-based causal relationship. Then, commits can be modeled as a directed graph model of causal relationships. This paper applies Google's PageRank algorithm to the graph modelin order to evaluate commits' influences on the others. Through an empirical study with Git repositories of six open source projects, the following factors are showed to be noteworthy:(1) the number of added files at the commit,(2) the length of commit message,(3) the experience of committing author, and (4) the number of developers who have been involved in the modified files at the commit.