paperbot · PL 论文追踪

全部论文 待读 收藏

P2 Automated Software Entity Matching Between Successive Versions

相邻版本间的自动化软件实体匹配
ASE 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)2023匹配度 2/10 · 论文研究跨版本软件实体匹配与重构发现,属于传统软件工程方向,未涉及LLM/Agent、浮点程序分析或贝叶斯程序分析,与用户研究画像的核心交叉点(PL与AI结合)无关。引用 9 inbox
Bo Liu, Hui Liu, Nan Niu, Yuxia Zhang, Guangjie Li, Yanjie Jiang
crossref: 2026-09-12dblp: 2026-09-12

AI 速览

一句话:提出迭代式实体匹配算法 ReMapper,大幅降低跨版本实体匹配错误并提升重构发现效果。

版本控制系统以文本行管理代码,无法呈现软件实体的演化,已有实体匹配算法准确率不足。本文提出自动化迭代算法 ReMapper,其核心洞察是限定名、实现与引用三者结合可区分实体;由于引用相似度依赖实体映射,而映射又依赖引用相似度,算法采用迭代匹配。作者在来自21个真实项目、215个提交的基准上评估,ReMapper 将错误(假阳性加假阴性)减少 85.8%,显著优于现有方法;在依赖实体匹配的自动重构发现任务中,召回率提升 6.9%,假阳性减少 72.6%。

问题
版本控制系统将代码视为纯文本,难以追踪软件实体在提交前后的演化;现有软件实体匹配算法准确率不足。
方法
提出迭代式 ReMapper,利用限定名、实现和引用三方面信息共同区分实体,并迭代更新映射与基于引用的相似度。
评估
在 21 个真实项目、215 个提交的基准上比较,错误减少 85.8%;并将其用于自动重构发现,召回率提升 6.9%、假阳性减少 72.6%。
原文摘要(Abstract)

Version control systems are widely used to manage the evolution of software applications. However, such version control systems take source code as lines of plain text, and thus they cannot present the evolution of software entities embedded in the source code. To this end, a few approaches have been proposed to match software entities before and after a given commit, known as software entity matching algorithms. However, the accuracy of such algorithms requires further improvement. In this paper, we propose an automated iterative algorithm (called ReMapper) to match software entities between two successive versions. The key insight of ReMapper is that the qualified name, the implementation, and the references of a software entity together can distinguish it from others. It matches software entities iteratively because the mapping depends on the reference-based similarity whereas the reference-based similarity depends on the mapping of entities as well. We evaluated ReMapper on a benchmark consisting of 215 commits from 21 real-world projects. Our evaluation results suggest that ReMapper substantially outperformed the state of the art, reducing the number of mistakes (false positives plus false negatives) substantially by 85.8%. We also evaluated to what extent it may improve the automated refactoring discovery (mining) that relies heavily on automated entity matching. Our evaluation results suggest that it substantially improved the state of the art in refactoring discovery, improving recall by 6.9% and reducing the number of false positives by 72.6%.

链接与引用

DOI 原文 · DBLP

BibTeX
@inproceedings{LiuLNZLJ23,
  title = {Automated Software Entity Matching Between Successive Versions},
  author = {Bo Liu and Hui Liu and Nan Niu and Yuxia Zhang and Guangjie Li and Yanjie Jiang},
  booktitle = {ASE},
  year = {2023},
  doi = {10.1109/ase56229.2023.00132}
}