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Integrating Issue Tracking Systems with Community-Based Question and Answering Websites

机译:将问题跟踪系统与基于社区的问答网站集成

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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.
机译:问题跟踪系统(例如Bugzilla)是促进软件维护专业人员之间协作的工具。流行的问题跟踪系统由讨论论坛组成,以促进错误报告和评论发布。我们观察到,问题跟踪系统中发布的一些评论包含指向外部网站的链接,例如YouTube(视频共享网站),Twitter(微博客网站),Stack Overflow(面向程序员的基于社区的问答网站),Wikipedia和重点关注网站。讨论论坛。堆栈溢出是面向程序员的流行的基于社区的问答网站,并且由于包含程序员在各种主题上发布的数百万个问题(广泛的知识资源)的答案,因此被软件工程师广泛使用。我们对开源Google Chromium和Android问题跟踪器数据(公开提供的真实世界数据集)进行了一系列实验,以了解堆栈溢出在问题解决中的作用和影响。我们的实验结果显示了在线程讨论中多次引用Stack Overflow的证据,并证明了存在Stack Overflow链接的较低平均修复时间(在一个数据集中)之间的相关性。我们还观察到,与不包含堆栈溢出引用的错误报告相比,提供堆栈溢出链接时响应错误报告而发布的评论的平均数量要少。我们基于类似的文本分析(基于内容的语言功能)和上下文数据分析(诸如与堆栈溢出问题相关的标记的被利用的元数据)进行实验,以为传入的错误报告推荐堆栈溢出问题。我们进行实证分析,以测量所提出方法在包含真实情况的数据集上的有效性,并提出我们的见解。我们介绍了一项针对Google Chromium Developers的调查结果,旨在了解从业人员的观点和经验。

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