JIRA中软件开发人员的情感面

Marco Ortu, Alessandro Murgia, Giuseppe Destefanis, Parastou Tourani, R. Tonelli, M. Marchesi, Bram Adams
{"title":"JIRA中软件开发人员的情感面","authors":"Marco Ortu, Alessandro Murgia, Giuseppe Destefanis, Parastou Tourani, R. Tonelli, M. Marchesi, Bram Adams","doi":"10.1145/2901739.2903505","DOIUrl":null,"url":null,"abstract":"ABSTRACTIssue tracking systems store valuable data for testing hy-potheses concerning maintenance, building statistical pre-diction models and (recently) investigating developer affec-tiveness. For the latter, issue tracking systems can be minedto explore developers emotions, sentiments and politeness, affects for short. However, research on affect detection insoftware artefacts is still in its early stage due to the lack ofmanually validated data and tools.In this paper, we contribute to the research of affectson software artefacts by providing a labeling of emotionspresent on issue comments.We manually labeled 2,000 issue comments and 4,000 sen-tences written by developers with emotions such as love,joy, surprise, anger, sadness and fear. Labeled commentsand sentences are linked to software artefacts reported inour previously published dataset (containing more than 1Kprojects, more than 700K issue reports and more than 2million issue comments). The enriched dataset presented inthis paper allows the investigation of the role of affects insoftware development.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"2017 1","pages":"480-483"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"98","resultStr":"{\"title\":\"The Emotional Side of Software Developers in JIRA\",\"authors\":\"Marco Ortu, Alessandro Murgia, Giuseppe Destefanis, Parastou Tourani, R. Tonelli, M. Marchesi, Bram Adams\",\"doi\":\"10.1145/2901739.2903505\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTIssue tracking systems store valuable data for testing hy-potheses concerning maintenance, building statistical pre-diction models and (recently) investigating developer affec-tiveness. For the latter, issue tracking systems can be minedto explore developers emotions, sentiments and politeness, affects for short. However, research on affect detection insoftware artefacts is still in its early stage due to the lack ofmanually validated data and tools.In this paper, we contribute to the research of affectson software artefacts by providing a labeling of emotionspresent on issue comments.We manually labeled 2,000 issue comments and 4,000 sen-tences written by developers with emotions such as love,joy, surprise, anger, sadness and fear. Labeled commentsand sentences are linked to software artefacts reported inour previously published dataset (containing more than 1Kprojects, more than 700K issue reports and more than 2million issue comments). The enriched dataset presented inthis paper allows the investigation of the role of affects insoftware development.\",\"PeriodicalId\":6621,\"journal\":{\"name\":\"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)\",\"volume\":\"2017 1\",\"pages\":\"480-483\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"98\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2901739.2903505\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2901739.2903505","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 98

摘要

问题跟踪系统存储有价值的数据,用于测试有关维护的假设,建立统计预测模型和(最近)调查开发人员的影响。对于后者,可以挖掘问题跟踪系统来探索开发人员的情绪、情绪和礼貌,简称影响。然而,由于缺乏人工验证的数据和工具,软件工件的影响检测研究仍处于早期阶段。在本文中,我们通过提供问题评论中存在的情感标签,为影响软件工件的研究做出了贡献。我们手动标记了2000条问题评论和4000个由开发者写的带有爱、喜悦、惊讶、愤怒、悲伤和恐惧等情绪的句子。标记的评论和句子链接到我们之前发布的数据集中报告的软件工件(包含超过1k个项目,超过700K个问题报告和超过200万个问题评论)。本文提供的丰富数据集允许调查影响在软件开发中的作用。
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
The Emotional Side of Software Developers in JIRA
ABSTRACTIssue tracking systems store valuable data for testing hy-potheses concerning maintenance, building statistical pre-diction models and (recently) investigating developer affec-tiveness. For the latter, issue tracking systems can be minedto explore developers emotions, sentiments and politeness, affects for short. However, research on affect detection insoftware artefacts is still in its early stage due to the lack ofmanually validated data and tools.In this paper, we contribute to the research of affectson software artefacts by providing a labeling of emotionspresent on issue comments.We manually labeled 2,000 issue comments and 4,000 sen-tences written by developers with emotions such as love,joy, surprise, anger, sadness and fear. Labeled commentsand sentences are linked to software artefacts reported inour previously published dataset (containing more than 1Kprojects, more than 700K issue reports and more than 2million issue comments). The enriched dataset presented inthis paper allows the investigation of the role of affects insoftware development.
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
MSR '20: 17th International Conference on Mining Software Repositories, Seoul, Republic of Korea, 29-30 June, 2020 Who you gonna call?: analyzing web requests in Android applications Cena słońca w projektowaniu architektonicznym Multi-extract and Multi-level Dataset of Mozilla Issue Tracking History Interactive Exploration of Developer Interaction Traces using a Hidden Markov Model
×
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