KeyTitle:通过关键字规划更好地生成错误报告标题

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Qianshuang Meng, Weiqin Zou, Biyu Cai, Jingxuan Zhang
{"title":"KeyTitle:通过关键字规划更好地生成错误报告标题","authors":"Qianshuang Meng, Weiqin Zou, Biyu Cai, Jingxuan Zhang","doi":"10.1007/s11219-024-09695-z","DOIUrl":null,"url":null,"abstract":"<p>Bug reports play an important role in the software development and maintenance process. As the eye of a bug report, a concise and fluent title is always preferred and expected by developers as it could help them quickly seize the problem point and make better decisions in handling the bugs. However, in practice, not all titles filled by bug reporters are found to be of high quality; some may not carry essential bug-related information, and some may be hard to understand or contain extra noise. With the aim to reduce the burden of bug reporters and ease developers’ life in handling bugs, we propose a deep learning-based technique named KeyTitle, to automatically generate a title for a given bug report. KeyTitle formulates the title generation problem as a one-sentence summarization task. It could be viewed as a Seq2Seq generation model (which generally directly generates target text based on source text) that incorporates keywords planning. Specifically, within KeyTitle, a transformer-based encoder-decoder model is enforced to generate a chain of keywords first from the detailed textual problem description, and then generate the target title by considering both these keywords and description content. Experiments over three large bug datasets collected from GitHub, Eclipse, and Apache shows that KeyTitle could outperform state-of-art title generation models relatively by up to 8.9-18.2<span>\\(\\%\\)</span>, 11.4-30.4<span>\\(\\%\\)</span>, and 13.0-18.0<span>\\(\\%\\)</span> in terms of ROUGE-1, ROUGE-2, and ROUGE-L F1-scores; the titles generated by KeyTitle are also found to be better in terms of Relevance, Accuracy, Conciseness, Fluency in human evaluation. Besides generating titles from textual descriptions, KeyTitle is also found to have great potential in generating titles based on just a few keywords, a task that also has much value in bug reporting/handling practice.</p>","PeriodicalId":21827,"journal":{"name":"Software Quality Journal","volume":"160 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"KeyTitle: towards better bug report title generation by keywords planning\",\"authors\":\"Qianshuang Meng, Weiqin Zou, Biyu Cai, Jingxuan Zhang\",\"doi\":\"10.1007/s11219-024-09695-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Bug reports play an important role in the software development and maintenance process. As the eye of a bug report, a concise and fluent title is always preferred and expected by developers as it could help them quickly seize the problem point and make better decisions in handling the bugs. However, in practice, not all titles filled by bug reporters are found to be of high quality; some may not carry essential bug-related information, and some may be hard to understand or contain extra noise. With the aim to reduce the burden of bug reporters and ease developers’ life in handling bugs, we propose a deep learning-based technique named KeyTitle, to automatically generate a title for a given bug report. KeyTitle formulates the title generation problem as a one-sentence summarization task. It could be viewed as a Seq2Seq generation model (which generally directly generates target text based on source text) that incorporates keywords planning. Specifically, within KeyTitle, a transformer-based encoder-decoder model is enforced to generate a chain of keywords first from the detailed textual problem description, and then generate the target title by considering both these keywords and description content. Experiments over three large bug datasets collected from GitHub, Eclipse, and Apache shows that KeyTitle could outperform state-of-art title generation models relatively by up to 8.9-18.2<span>\\\\(\\\\%\\\\)</span>, 11.4-30.4<span>\\\\(\\\\%\\\\)</span>, and 13.0-18.0<span>\\\\(\\\\%\\\\)</span> in terms of ROUGE-1, ROUGE-2, and ROUGE-L F1-scores; the titles generated by KeyTitle are also found to be better in terms of Relevance, Accuracy, Conciseness, Fluency in human evaluation. Besides generating titles from textual descriptions, KeyTitle is also found to have great potential in generating titles based on just a few keywords, a task that also has much value in bug reporting/handling practice.</p>\",\"PeriodicalId\":21827,\"journal\":{\"name\":\"Software Quality Journal\",\"volume\":\"160 1\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Quality Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11219-024-09695-z\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Quality Journal","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11219-024-09695-z","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

摘要

错误报告在软件开发和维护过程中发挥着重要作用。作为错误报告的 "眼睛",简洁流畅的标题总是受到开发人员的青睐和期待,因为它可以帮助他们快速抓住问题点,在处理错误时做出更好的决策。然而,在实践中,并不是所有错误报告者填写的标题都是高质量的,有些标题可能没有包含与错误相关的基本信息,有些标题可能难以理解或包含额外的噪音。为了减轻错误报告者的负担,方便开发人员处理错误,我们提出了一种基于深度学习的技术--KeyTitle,用于为给定的错误报告自动生成标题。KeyTitle 将标题生成问题表述为一句话总结任务。它可以被看作是一个结合了关键词规划的 Seq2Seq 生成模型(一般直接根据源文本生成目标文本)。具体来说,在 KeyTitle 中,基于转换器的编码器-解码器模型被强制执行,首先从详细的文本问题描述中生成关键词链,然后通过考虑这些关键词和描述内容生成目标标题。对从 GitHub、Eclipse 和 Apache 收集的三个大型 bug 数据集进行的实验表明,KeyTitle 的性能相对优于最先进的标题生成模型,最高可达 8.9-18.2(\%\)、11.4-30.在人类评估中,KeyTitle 生成的标题在相关性、准确性、简洁性和流畅性方面也更胜一筹。除了根据文字描述生成标题外,KeyTitle 在根据几个关键词生成标题方面也有很大潜力,这在错误报告/处理实践中也很有价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

KeyTitle: towards better bug report title generation by keywords planning

KeyTitle: towards better bug report title generation by keywords planning

Bug reports play an important role in the software development and maintenance process. As the eye of a bug report, a concise and fluent title is always preferred and expected by developers as it could help them quickly seize the problem point and make better decisions in handling the bugs. However, in practice, not all titles filled by bug reporters are found to be of high quality; some may not carry essential bug-related information, and some may be hard to understand or contain extra noise. With the aim to reduce the burden of bug reporters and ease developers’ life in handling bugs, we propose a deep learning-based technique named KeyTitle, to automatically generate a title for a given bug report. KeyTitle formulates the title generation problem as a one-sentence summarization task. It could be viewed as a Seq2Seq generation model (which generally directly generates target text based on source text) that incorporates keywords planning. Specifically, within KeyTitle, a transformer-based encoder-decoder model is enforced to generate a chain of keywords first from the detailed textual problem description, and then generate the target title by considering both these keywords and description content. Experiments over three large bug datasets collected from GitHub, Eclipse, and Apache shows that KeyTitle could outperform state-of-art title generation models relatively by up to 8.9-18.2\(\%\), 11.4-30.4\(\%\), and 13.0-18.0\(\%\) in terms of ROUGE-1, ROUGE-2, and ROUGE-L F1-scores; the titles generated by KeyTitle are also found to be better in terms of Relevance, Accuracy, Conciseness, Fluency in human evaluation. Besides generating titles from textual descriptions, KeyTitle is also found to have great potential in generating titles based on just a few keywords, a task that also has much value in bug reporting/handling practice.

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
Software Quality Journal
Software Quality Journal 工程技术-计算机:软件工程
CiteScore
4.90
自引率
5.30%
发文量
26
审稿时长
>12 weeks
期刊介绍: The aims of the Software Quality Journal are: (1) To promote awareness of the crucial role of quality management in the effective construction of the software systems developed, used, and/or maintained by organizations in pursuit of their business objectives. (2) To provide a forum of the exchange of experiences and information on software quality management and the methods, tools and products used to measure and achieve it. (3) To provide a vehicle for the publication of academic papers related to all aspects of software quality. The Journal addresses all aspects of software quality from both a practical and an academic viewpoint. It invites contributions from practitioners and academics, as well as national and international policy and standard making bodies, and sets out to be the definitive international reference source for such information. The Journal will accept research, technique, case study, survey and tutorial submissions that address quality-related issues including, but not limited to: internal and external quality standards, management of quality within organizations, technical aspects of quality, quality aspects for product vendors, software measurement and metrics, software testing and other quality assurance techniques, total quality management and cultural aspects. Other technical issues with regard to software quality, including: data management, formal methods, safety critical applications, and CASE.
×
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:481959085
Book学术官方微信