TraceRefiner:一种用于细化粗粒度需求到类跟踪的自动化技术

Mouna Hammoudi, Christoph Mayr-Dorn, A. Mashkoor, Alexander Egyed
{"title":"TraceRefiner:一种用于细化粗粒度需求到类跟踪的自动化技术","authors":"Mouna Hammoudi, Christoph Mayr-Dorn, A. Mashkoor, Alexander Egyed","doi":"10.1109/APSEC53868.2021.00009","DOIUrl":null,"url":null,"abstract":"Requirement-to-code traces reveal the code location(s) where a requirement is implemented. Traceability is essential for code evolution and understanding. However, creating and maintaining requirement-to-code traces is a tedious and costly process. In this paper, we introduce TraceRefiner, a novel technique for automatically refining coarse-grained requirement-to-class traces to fine-grained requirement-to-method traces. The inputs of TraceRefiner are (1) the set of requirement-to-class traces, which are easier to create as there are far fewer traces to capture, and (2) information about the code structure (i.e., method calls). The output of TraceRefiner is the set of requirement-to-method traces (providing additional, fine-grained information to the developer). We demonstrate the quality of TraceRefiner on four case study systems (7-72KLOC) and evaluated it on over 230,000 requirement-to-method predictions. The evaluation demonstrates TraceRefiner's ability to refine traces even if many requirement-to-class traces are undefined (incomplete input). The obtained results show that the proposed technique is fully automated, tool-supported, and scalable.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"TraceRefiner: An Automated Technique for Refining Coarse-Grained Requirement-to-Class Traces\",\"authors\":\"Mouna Hammoudi, Christoph Mayr-Dorn, A. Mashkoor, Alexander Egyed\",\"doi\":\"10.1109/APSEC53868.2021.00009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Requirement-to-code traces reveal the code location(s) where a requirement is implemented. Traceability is essential for code evolution and understanding. However, creating and maintaining requirement-to-code traces is a tedious and costly process. In this paper, we introduce TraceRefiner, a novel technique for automatically refining coarse-grained requirement-to-class traces to fine-grained requirement-to-method traces. The inputs of TraceRefiner are (1) the set of requirement-to-class traces, which are easier to create as there are far fewer traces to capture, and (2) information about the code structure (i.e., method calls). The output of TraceRefiner is the set of requirement-to-method traces (providing additional, fine-grained information to the developer). We demonstrate the quality of TraceRefiner on four case study systems (7-72KLOC) and evaluated it on over 230,000 requirement-to-method predictions. The evaluation demonstrates TraceRefiner's ability to refine traces even if many requirement-to-class traces are undefined (incomplete input). The obtained results show that the proposed technique is fully automated, tool-supported, and scalable.\",\"PeriodicalId\":143800,\"journal\":{\"name\":\"2021 28th Asia-Pacific Software Engineering Conference (APSEC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 28th Asia-Pacific Software Engineering Conference (APSEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APSEC53868.2021.00009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSEC53868.2021.00009","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

需求到代码的跟踪显示了实现需求的代码位置。可追溯性对于代码进化和理解是必不可少的。然而,创建和维护从需求到代码的跟踪是一个冗长而昂贵的过程。在本文中,我们将介绍TraceRefiner,这是一种新技术,用于自动将粗粒度的需求到类的跟踪细化为细粒度的需求到方法的跟踪。TraceRefiner的输入是(1)从需求到类的跟踪的集合,它更容易创建,因为需要捕获的跟踪要少得多,以及(2)关于代码结构的信息(例如,方法调用)。TraceRefiner的输出是一组从需求到方法的跟踪(为开发人员提供额外的细粒度信息)。我们在四个案例研究系统(7-72KLOC)上展示了TraceRefiner的质量,并在超过230,000个需求到方法的预测中对其进行了评估。评估证明了TraceRefiner细化跟踪的能力,即使许多需求到类的跟踪是未定义的(不完整的输入)。实验结果表明,该方法具有自动化程度高、工具支持强、可扩展性好等特点。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
TraceRefiner: An Automated Technique for Refining Coarse-Grained Requirement-to-Class Traces
Requirement-to-code traces reveal the code location(s) where a requirement is implemented. Traceability is essential for code evolution and understanding. However, creating and maintaining requirement-to-code traces is a tedious and costly process. In this paper, we introduce TraceRefiner, a novel technique for automatically refining coarse-grained requirement-to-class traces to fine-grained requirement-to-method traces. The inputs of TraceRefiner are (1) the set of requirement-to-class traces, which are easier to create as there are far fewer traces to capture, and (2) information about the code structure (i.e., method calls). The output of TraceRefiner is the set of requirement-to-method traces (providing additional, fine-grained information to the developer). We demonstrate the quality of TraceRefiner on four case study systems (7-72KLOC) and evaluated it on over 230,000 requirement-to-method predictions. The evaluation demonstrates TraceRefiner's ability to refine traces even if many requirement-to-class traces are undefined (incomplete input). The obtained results show that the proposed technique is fully automated, tool-supported, and scalable.
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
期刊最新文献
Verification Assisted Gas Reduction for Smart Contracts Effective Bug Triage Based on a Hybrid Neural Network Learn To Align: A Code Alignment Network For Code Clone Detection Framework for Recommending Data Residency Compliant Application Architecture Degree doesn't Matter: Identifying the Drivers of Interaction in Software Development Ecosystems
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1