使用动态和上下文特性来预测GitHub项目中的问题生命周期

R. Kikas, M. Dumas, Dietmar Pfahl
{"title":"使用动态和上下文特性来预测GitHub项目中的问题生命周期","authors":"R. Kikas, M. Dumas, Dietmar Pfahl","doi":"10.1145/2901739.2901751","DOIUrl":null,"url":null,"abstract":"Methods for predicting issue lifetime can help software project managers to prioritize issues and allocate resources accordingly. Previous studies on issue lifetime prediction have focused on models built from static features, meaning features calculated at one snapshot of the issue's lifetime based on data associated to the issue itself. However, during its lifetime, an issue typically receives comments from various stakeholders, which may carry valuable insights into its perceived priority and difficulty and may thus be exploited to update lifetime predictions. Moreover, the lifetime of an issue depends not only on characteristics of the issue itself, but also on the state of the project as a whole. Hence, issue lifetime prediction may benefit from taking into account features capturing the issue's context (contextual features). In this work, we analyze issues from more than 4000 GitHub projects and build models to predict, at different points in an issue's lifetime, whether or not the issue will close within a given calendric period, by combining static, dynamic and contextual features. The results show that dynamic and contextual features complement the predictive power of static ones, particularly for long-term predictions.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"118 1","pages":"291-302"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"72","resultStr":"{\"title\":\"Using Dynamic and Contextual Features to Predict Issue Lifetime in GitHub Projects\",\"authors\":\"R. Kikas, M. Dumas, Dietmar Pfahl\",\"doi\":\"10.1145/2901739.2901751\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Methods for predicting issue lifetime can help software project managers to prioritize issues and allocate resources accordingly. Previous studies on issue lifetime prediction have focused on models built from static features, meaning features calculated at one snapshot of the issue's lifetime based on data associated to the issue itself. However, during its lifetime, an issue typically receives comments from various stakeholders, which may carry valuable insights into its perceived priority and difficulty and may thus be exploited to update lifetime predictions. Moreover, the lifetime of an issue depends not only on characteristics of the issue itself, but also on the state of the project as a whole. Hence, issue lifetime prediction may benefit from taking into account features capturing the issue's context (contextual features). In this work, we analyze issues from more than 4000 GitHub projects and build models to predict, at different points in an issue's lifetime, whether or not the issue will close within a given calendric period, by combining static, dynamic and contextual features. The results show that dynamic and contextual features complement the predictive power of static ones, particularly for long-term predictions.\",\"PeriodicalId\":6621,\"journal\":{\"name\":\"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)\",\"volume\":\"118 1\",\"pages\":\"291-302\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"72\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2901739.2901751\",\"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/10.1145/2901739.2901751","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 72

摘要

预测问题生命周期的方法可以帮助软件项目经理对问题进行优先排序,并相应地分配资源。之前关于问题生命周期预测的研究主要集中在基于静态特征的模型上,即基于与问题本身相关的数据,在问题生命周期的一个快照中计算出的特征。然而,在其生命周期中,一个问题通常会收到来自不同涉众的评论,这些评论可能会对其感知到的优先级和难度产生有价值的见解,因此可能会被用来更新生命周期预测。此外,问题的持续时间不仅取决于问题本身的特征,还取决于整个项目的状态。因此,考虑到捕获问题上下文的特性(上下文特性),问题生命周期预测可能会受益。在这项工作中,我们分析了来自4000多个GitHub项目的问题,并建立了模型,通过结合静态、动态和上下文特征,在问题生命周期的不同时间点预测问题是否会在给定的日历期内结束。结果表明,动态和上下文特征补充了静态特征的预测能力,特别是对于长期预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Dynamic and Contextual Features to Predict Issue Lifetime in GitHub Projects
Methods for predicting issue lifetime can help software project managers to prioritize issues and allocate resources accordingly. Previous studies on issue lifetime prediction have focused on models built from static features, meaning features calculated at one snapshot of the issue's lifetime based on data associated to the issue itself. However, during its lifetime, an issue typically receives comments from various stakeholders, which may carry valuable insights into its perceived priority and difficulty and may thus be exploited to update lifetime predictions. Moreover, the lifetime of an issue depends not only on characteristics of the issue itself, but also on the state of the project as a whole. Hence, issue lifetime prediction may benefit from taking into account features capturing the issue's context (contextual features). In this work, we analyze issues from more than 4000 GitHub projects and build models to predict, at different points in an issue's lifetime, whether or not the issue will close within a given calendric period, by combining static, dynamic and contextual features. The results show that dynamic and contextual features complement the predictive power of static ones, particularly for long-term predictions.
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