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}
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.