{"title":"Integrating Issue Tracking Systems with Community-Based Question and Answering Websites","authors":"D. Correa, A. Sureka","doi":"10.1109/ASWEC.2013.20","DOIUrl":null,"url":null,"abstract":"Issue tracking systems such as Bugzilla are tools to facilitate collaboration between software maintenance professionals. Popular issue tracking systems consists of discussion forums to facilitate bug reporting and comment posting. We observe that several comments posted in issue tracking system contains link to external websites such as YouTube (video sharing website), Twitter (micro-blogging website), Stack overflow (a community-based question and answering website for programmers), Wikipedia and focused discussions forums. Stack overflow is a popular community-based question and answering website for programmers and is widely used by software engineers as it contains answers to millions of questions (an extensive knowledge resource) posted by programmers on diverse topics. We conduct a series of experiments on open-source Google Chromium and Android issue tracker data (publicly available real-world dataset) to understand the role and impact of Stack overflow in issue resolution. Our experimental results show evidences of several references to Stack overflow in threaded discussions and demonstrate correlation between a lower mean time to repair (in one dataset) with presence of Stack overflow links. We also observe that the average number of comments posted in response to bug reports are less when Stack overflow links are presented in contrast to bug reports not containing Stack overflow references. We conduct experiments based on textual similarly analysis (content-based linguistic features) and contextual data analysis (exploited metadata such as tags associated to a Stack overflow question) to recommend Stack overflow questions for an incoming bug report. We perform empirical analysis to measure the effectiveness of the proposed method on a dataset containing ground-truth and present our insights. We present the result of a survey (of Google Chromium Developers) that we conducted to understand practitioner's perspective and experience.","PeriodicalId":394020,"journal":{"name":"2013 22nd Australian Software Engineering Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 22nd Australian Software Engineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASWEC.2013.20","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19
Abstract
Issue tracking systems such as Bugzilla are tools to facilitate collaboration between software maintenance professionals. Popular issue tracking systems consists of discussion forums to facilitate bug reporting and comment posting. We observe that several comments posted in issue tracking system contains link to external websites such as YouTube (video sharing website), Twitter (micro-blogging website), Stack overflow (a community-based question and answering website for programmers), Wikipedia and focused discussions forums. Stack overflow is a popular community-based question and answering website for programmers and is widely used by software engineers as it contains answers to millions of questions (an extensive knowledge resource) posted by programmers on diverse topics. We conduct a series of experiments on open-source Google Chromium and Android issue tracker data (publicly available real-world dataset) to understand the role and impact of Stack overflow in issue resolution. Our experimental results show evidences of several references to Stack overflow in threaded discussions and demonstrate correlation between a lower mean time to repair (in one dataset) with presence of Stack overflow links. We also observe that the average number of comments posted in response to bug reports are less when Stack overflow links are presented in contrast to bug reports not containing Stack overflow references. We conduct experiments based on textual similarly analysis (content-based linguistic features) and contextual data analysis (exploited metadata such as tags associated to a Stack overflow question) to recommend Stack overflow questions for an incoming bug report. We perform empirical analysis to measure the effectiveness of the proposed method on a dataset containing ground-truth and present our insights. We present the result of a survey (of Google Chromium Developers) that we conducted to understand practitioner's perspective and experience.