根据用户评论推荐和本地化移动应用的变更请求

Fabio Palomba, P. Salza, Adelina Ciurumelea, Sebastiano Panichella, H. Gall, F. Ferrucci, A. D. Lucia
{"title":"根据用户评论推荐和本地化移动应用的变更请求","authors":"Fabio Palomba, P. Salza, Adelina Ciurumelea, Sebastiano Panichella, H. Gall, F. Ferrucci, A. D. Lucia","doi":"10.1109/ICSE.2017.18","DOIUrl":null,"url":null,"abstract":"Researchers have proposed several approaches to extract information from user reviews useful for maintaining and evolving mobile apps. However, most of them just perform automatic classification of user reviews according to specific keywords (e.g., bugs, features). Moreover, they do not provide any support for linking user feedback to the source code components to be changed, thus requiring a manual, time-consuming, and error-prone task. In this paper, we introduce ChangeAdvisor, a novel approach that analyzes the structure, semantics, and sentiments of sentences contained in user reviews to extract useful (user) feedback from maintenance perspectives and recommend to developers changes to software artifacts. It relies on natural language processing and clustering algorithms to group user reviews around similar user needs and suggestions for change. Then, it involves textual based heuristics to determine the code artifacts that need to be maintained according to the recommended software changes. The quantitative and qualitative studies carried out on 44,683 user reviews of 10 open source mobile apps and their original developers showed a high accuracy of ChangeAdvisor in (i) clustering similar user change requests and (ii) identifying the code components impacted by the suggested changes. Moreover, the obtained results show that ChangeAdvisor is more accurate than a baseline approach for linking user feedback clusters to the source code in terms of both precision (+47%) and recall (+38%).","PeriodicalId":6505,"journal":{"name":"2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)","volume":"76 1","pages":"106-117"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"136","resultStr":"{\"title\":\"Recommending and Localizing Change Requests for Mobile Apps Based on User Reviews\",\"authors\":\"Fabio Palomba, P. Salza, Adelina Ciurumelea, Sebastiano Panichella, H. Gall, F. Ferrucci, A. D. Lucia\",\"doi\":\"10.1109/ICSE.2017.18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Researchers have proposed several approaches to extract information from user reviews useful for maintaining and evolving mobile apps. However, most of them just perform automatic classification of user reviews according to specific keywords (e.g., bugs, features). Moreover, they do not provide any support for linking user feedback to the source code components to be changed, thus requiring a manual, time-consuming, and error-prone task. In this paper, we introduce ChangeAdvisor, a novel approach that analyzes the structure, semantics, and sentiments of sentences contained in user reviews to extract useful (user) feedback from maintenance perspectives and recommend to developers changes to software artifacts. It relies on natural language processing and clustering algorithms to group user reviews around similar user needs and suggestions for change. Then, it involves textual based heuristics to determine the code artifacts that need to be maintained according to the recommended software changes. The quantitative and qualitative studies carried out on 44,683 user reviews of 10 open source mobile apps and their original developers showed a high accuracy of ChangeAdvisor in (i) clustering similar user change requests and (ii) identifying the code components impacted by the suggested changes. Moreover, the obtained results show that ChangeAdvisor is more accurate than a baseline approach for linking user feedback clusters to the source code in terms of both precision (+47%) and recall (+38%).\",\"PeriodicalId\":6505,\"journal\":{\"name\":\"2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)\",\"volume\":\"76 1\",\"pages\":\"106-117\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"136\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSE.2017.18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSE.2017.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 136

摘要

研究人员提出了几种从用户评论中提取信息的方法,这些信息对维护和发展移动应用程序很有用。然而,他们中的大多数只是根据特定的关键字(例如,bug,功能)对用户评论进行自动分类。此外,它们不提供将用户反馈链接到要更改的源代码组件的任何支持,因此需要手动、耗时且容易出错的任务。在本文中,我们介绍了ChangeAdvisor,这是一种新颖的方法,它分析用户评论中包含的句子的结构、语义和情感,从维护的角度提取有用的(用户)反馈,并向开发人员推荐对软件工件的更改。它依靠自然语言处理和聚类算法,围绕类似的用户需求和更改建议对用户评论进行分组。然后,它涉及到基于文本的启发式方法,以根据推荐的软件更改确定需要维护的代码工件。对10个开源移动应用及其原始开发者的44,683条用户评论进行的定量和定性研究表明,ChangeAdvisor在(i)聚类类似用户更改请求和(ii)识别受建议更改影响的代码组件方面具有很高的准确性。此外,所获得的结果表明,在精度(+47%)和召回率(+38%)方面,ChangeAdvisor比基线方法更准确地将用户反馈聚类链接到源代码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recommending and Localizing Change Requests for Mobile Apps Based on User Reviews
Researchers have proposed several approaches to extract information from user reviews useful for maintaining and evolving mobile apps. However, most of them just perform automatic classification of user reviews according to specific keywords (e.g., bugs, features). Moreover, they do not provide any support for linking user feedback to the source code components to be changed, thus requiring a manual, time-consuming, and error-prone task. In this paper, we introduce ChangeAdvisor, a novel approach that analyzes the structure, semantics, and sentiments of sentences contained in user reviews to extract useful (user) feedback from maintenance perspectives and recommend to developers changes to software artifacts. It relies on natural language processing and clustering algorithms to group user reviews around similar user needs and suggestions for change. Then, it involves textual based heuristics to determine the code artifacts that need to be maintained according to the recommended software changes. The quantitative and qualitative studies carried out on 44,683 user reviews of 10 open source mobile apps and their original developers showed a high accuracy of ChangeAdvisor in (i) clustering similar user change requests and (ii) identifying the code components impacted by the suggested changes. Moreover, the obtained results show that ChangeAdvisor is more accurate than a baseline approach for linking user feedback clusters to the source code in terms of both precision (+47%) and recall (+38%).
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:481959085
Book学术官方微信