整合问题跟踪系统与基于社区的问题和回答网站

D. Correa, A. Sureka
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引用次数: 19

摘要

Bugzilla等问题跟踪系统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,在问题跟踪系统中发布的一些评论包含外部网站的链接,如YouTube(视频分享网站)、Twitter(微博网站)、Stack overflow(程序员社区问答网站)、Wikipedia和焦点论坛。Stack overflow是一个流行的基于社区的程序员问答网站,它被软件工程师广泛使用,因为它包含了程序员在不同主题上发布的数百万个问题的答案(一个广泛的知识资源)。我们在开源谷歌Chromium和Android问题跟踪数据(公开可用的真实数据集)上进行了一系列实验,以了解Stack overflow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堆栈溢出的证据,并证明了(在一个数据集中)较低的平均修复时间与堆栈溢出链接的存在之间的相关性。我们还观察到,与不包含堆栈溢出引用的错误报告相比,当出现堆栈溢出链接时,针对错误报告发布的评论的平均数量要少一些。我们进行了基于文本相似分析(基于内容的语言特征)和上下文数据分析(利用元数据,如与堆栈溢出问题相关的标签)的实验,为传入的bug报告推荐堆栈溢出问题。我们进行了实证分析,以衡量所提出的方法在包含基础真理的数据集上的有效性,并提出了我们的见解。我们提供了一项调查的结果(谷歌Chromium开发人员),我们进行了这项调查,以了解从业者的观点和经验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Issue Tracking Systems with Community-Based Question and Answering Websites
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.
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