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P2 Salient-class location: help developers understand code change in code review

显著类定位:帮助开发者在代码审查中理解代码变更
FSE Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering2018匹配度 2/10 · 该论文将机器学习用于代码审查中的显著类定位,属于机器学习与程序语言的交叉,但既不涉及静态分析、LLM/Agent,也不涉及浮点程序分析或贝叶斯程序分析,与用户核心研究方向仅有非常间接的关联。引用 25 inbox
Yuan Huang, Nan Jia, Xiangping Chen, Kai Hong, Zibin Zheng
crossref: 2026-09-12

AI 速览

一句话:提出显著类定位方法,将被重点修改并引发其他类改动的类识别为二分类问题,帮助审查者理解代码变更。

代码审查需要投入大量人力来理解代码变更,因为审查所需信息往往分散在审查者不熟悉的多个文件中,而变更通常以 commit 为单位组织提交。作者发现大多数 commit 中存在一个“显著类”:它被显著修改,并导致同一 commit 中其余类的改动。用户研究证实,识别出 commit 中的显著类有助于审查者理解代码变更。为此,作者将显著类识别建模为二分类问题,从 commit 中抽取多种具有判别力的特征来刻画类的显著性。初步实验结果表明,所提方法能够提升审查者理解代码变更的效率。

问题
代码审查中变更信息分散在多个陌生文件里,审查者需要大量精力才能理解一个 commit 的改动意图。
方法
发现多数 commit 含有一个被显著修改且驱动其余类改动的“显著类”,将其识别形式化为二分类任务,并从 commit 中提取判别性特征刻画类的显著性。
评估
通过用户研究验证识别显著类对理解代码变更的帮助,并用初步实验评估该方法提升审查者理解效率的效果。
原文摘要(Abstract)

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, 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. We model the salient class identification as a binary classification problem and extract a number of discriminative features from commit to characterize the salience of a class. The initial experiment result shows that the proposed approach can improve the efficiency of reviewers understanding code changes in code review.

链接与引用

DOI 原文 ·

BibTeX
@inproceedings{paperbot4583,
  title = {Salient-class location: help developers understand code change in code review},
  author = {Yuan Huang and Nan Jia and Xiangping Chen and Kai Hong and Zibin Zheng},
  booktitle = {FSE},
  year = {2018},
  doi = {10.1145/3236024.3264841}
}