PR-SZZ:拉取请求如何支持软件存储库中的缺陷跟踪

P. Bludau, A. Pretschner
{"title":"PR-SZZ:拉取请求如何支持软件存储库中的缺陷跟踪","authors":"P. Bludau, A. Pretschner","doi":"10.1109/SANER53432.2022.00012","DOIUrl":null,"url":null,"abstract":"The SZZ algorithm represents a standard way to identify bug fixing commits as well as inducing counterparts. It forms the basis for data sets used in numerous empirical studies. Since its creation, multiple extensions have been proposed to enhance its performance. For historical reasons, related work relies on commit messages to map bug tickets to possibly related code with no additional data used to trace inducing commits from these fixes. Therefore, we present an updated version of SZZ utilizing pull requests, which are widely adopted today. We evaluate our approach in comparison to existing SZZ variants by conducting experiments and analyzing the usage of pull requests, inner commits, and merge strategies. We base our results on 6 open-source projects with more than 50k commits and 35k pull requests. With respect to bug fixing commits, on average 18% of bug tickets can be additionally mapped to a fixing commit, resulting in an overall F-score of 0.75, an improvement of 40 percentage points. By selecting an inducing commit, we manage to reduce the false-positives and increase precision by on average 16 percentage points in comparison to existing approaches.","PeriodicalId":437520,"journal":{"name":"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"PR-SZZ: How pull requests can support the tracing of defects in software repositories\",\"authors\":\"P. Bludau, A. Pretschner\",\"doi\":\"10.1109/SANER53432.2022.00012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The SZZ algorithm represents a standard way to identify bug fixing commits as well as inducing counterparts. It forms the basis for data sets used in numerous empirical studies. Since its creation, multiple extensions have been proposed to enhance its performance. For historical reasons, related work relies on commit messages to map bug tickets to possibly related code with no additional data used to trace inducing commits from these fixes. Therefore, we present an updated version of SZZ utilizing pull requests, which are widely adopted today. We evaluate our approach in comparison to existing SZZ variants by conducting experiments and analyzing the usage of pull requests, inner commits, and merge strategies. We base our results on 6 open-source projects with more than 50k commits and 35k pull requests. With respect to bug fixing commits, on average 18% of bug tickets can be additionally mapped to a fixing commit, resulting in an overall F-score of 0.75, an improvement of 40 percentage points. By selecting an inducing commit, we manage to reduce the false-positives and increase precision by on average 16 percentage points in comparison to existing approaches.\",\"PeriodicalId\":437520,\"journal\":{\"name\":\"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SANER53432.2022.00012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SANER53432.2022.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

SZZ算法代表了一种识别bug修复提交和诱导提交的标准方法。它构成了许多实证研究中使用的数据集的基础。自创建以来,已经提出了多个扩展来增强其性能。由于历史原因,相关工作依赖于提交消息来将bug票据映射到可能相关的代码,而没有使用额外的数据来跟踪这些修复的诱导提交。因此,我们提出了一个更新版本的SZZ利用拉请求,这是广泛采用的今天。我们通过进行实验和分析拉请求、内部提交和合并策略的使用情况,将我们的方法与现有的SZZ变体进行比较。我们的结果基于6个开源项目,这些项目有超过5万次提交和3.5万次拉取请求。对于bug修复提交,平均有18%的bug票可以被额外映射到bug修复提交,从而得到0.75的f分,提高了40个百分点。通过选择诱导提交,与现有方法相比,我们设法减少了误报,并将精度平均提高了16个百分点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PR-SZZ: How pull requests can support the tracing of defects in software repositories
The SZZ algorithm represents a standard way to identify bug fixing commits as well as inducing counterparts. It forms the basis for data sets used in numerous empirical studies. Since its creation, multiple extensions have been proposed to enhance its performance. For historical reasons, related work relies on commit messages to map bug tickets to possibly related code with no additional data used to trace inducing commits from these fixes. Therefore, we present an updated version of SZZ utilizing pull requests, which are widely adopted today. We evaluate our approach in comparison to existing SZZ variants by conducting experiments and analyzing the usage of pull requests, inner commits, and merge strategies. We base our results on 6 open-source projects with more than 50k commits and 35k pull requests. With respect to bug fixing commits, on average 18% of bug tickets can be additionally mapped to a fixing commit, resulting in an overall F-score of 0.75, an improvement of 40 percentage points. By selecting an inducing commit, we manage to reduce the false-positives and increase precision by on average 16 percentage points in comparison to existing approaches.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信