基于变更历史学习的软件影响分析工具及其评价

2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) Pub Date : 2022-05-01 DOI:10.1145/3510457.3519017

H. Iwasaki, Tsuyoshi Nakajima, Ryota Tsukamoto, Kazuko Takahashi, Shuichi Tokumoto

{"title":"基于变更历史学习的软件影响分析工具及其评价","authors":"H. Iwasaki, Tsuyoshi Nakajima, Ryota Tsukamoto, Kazuko Takahashi, Shuichi Tokumoto","doi":"10.1145/3510457.3519017","DOIUrl":null,"url":null,"abstract":"Software change impact analysis plays an important role in controlling software evolution in the maintenance of continuous software development. We developed a tool for change impact analysis, which machine-learns change histories and directly outputs candidates of the components to be modified for a change request. We applied the tool to real project data to evaluate it with two metrics: coverage range ratio and accuracy in the coverage range. The results show that it works well for software projects having many change histories for one source code base.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Software Impact Analysis Tool based on Change History Learning and its Evaluation\",\"authors\":\"H. Iwasaki, Tsuyoshi Nakajima, Ryota Tsukamoto, Kazuko Takahashi, Shuichi Tokumoto\",\"doi\":\"10.1145/3510457.3519017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Software change impact analysis plays an important role in controlling software evolution in the maintenance of continuous software development. We developed a tool for change impact analysis, which machine-learns change histories and directly outputs candidates of the components to be modified for a change request. We applied the tool to real project data to evaluate it with two metrics: coverage range ratio and accuracy in the coverage range. The results show that it works well for software projects having many change histories for one source code base.\",\"PeriodicalId\":119790,\"journal\":{\"name\":\"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3510457.3519017\",\"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/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3510457.3519017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

软件变更影响分析在软件持续开发的维护中，对控制软件演进起着重要的作用。我们开发了一个用于变更影响分析的工具，该工具可以通过机器学习变更历史，并直接输出要为变更请求修改的候选组件。我们将该工具应用到实际的项目数据中，用两个度量来评估它:覆盖范围比率和覆盖范围中的准确性。结果表明，对于一个源代码库具有许多变更历史的软件项目，它可以很好地工作。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Software Impact Analysis Tool based on Change History Learning and its Evaluation

Software change impact analysis plays an important role in controlling software evolution in the maintenance of continuous software development. We developed a tool for change impact analysis, which machine-learns change histories and directly outputs candidates of the components to be modified for a change request. We applied the tool to real project data to evaluate it with two metrics: coverage range ratio and accuracy in the coverage range. The results show that it works well for software projects having many change histories for one source code base.

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

2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

自引率

0.00%

发文量