Code Review Knowledge Perception: Fusing Multi-Features for Salient-Class Location

Abstract

Code review is a common software engineering practice of practical importance to reduce software defects. Review today is often with the help of specialized tools, such as Gerrit. However, even in a tool-supported code review involves a significant amount of human effort to understand the code change, because the information required to inspect code changes may distribute across multiple files that reviewers are not familiar with. Code changes are often organized as commits for review. In this paper, we found that most of the commits contain a salient class(es), which is saliently modified and causes the modification of the rest classes in a commit. Our user studies confirmed that identifying the salient class in a commit can facilitate reviewers in understanding code change. Inspired by the effectiveness of machine learning techniques in the classification field, we model the salient class identification as a binary classification problem and a number of discriminative features is extracted for a commit and used to characterize the salience of a class. The experiments results show that our approach achieves an accuracy of 88 percent. A user study with industrial developers shows that our approach can really improve the efficiency of reviewers understanding code changes in a reviewing scenario without using comment.