利用社交网络对bug进行分类：对四个开源软件社区的案例研究
Categorizing Bugs with Social Networks: A Case Study on Four Open Source Software Communities
摘要：
有效的错误分类过程是成功的协作软件工程项目的重要前提。对bug进行分类可能会成为一项艰巨的任务，特别是在开源软件（OSS）项目中，有大量缺乏经验的兼职贡献者。在本文中，我们提出了一个有效的和实用的方法来识别有效的错误报告，a）指的是一个实际的软件错误，b）不是重复的，c）包含足够的信息，立即处理。我们的分类是基于九个措施，以量化的社会嵌入的bug报告者在协作网络。我们证明了它的适用性的案例研究中，使用一个全面的数据集，超过70万个错误报告，从Bugzilla安装的四个主要的OSS社区，为期十多年。对于那些表现出最低比例的有效错误报告的项目，我们发现，错误报告者在协作网络中的位置是错误报告质量的一个强有力的指标。基于这一发现，我们开发了一个自动化的分类方案，可以很容易地集成到错误跟踪平台，并分析其性能在考虑OSS社区。一个支持向量机（SVM），以确定有效的错误报告的基础上的九个措施产生了高达90.3%的准确率与38.9%的召回。有了这个，我们显着提高了在以前的案例研究中获得的结果，自动早期识别的错误，最终修复。此外，我们的研究强调了在协作软件工程中使用社会组织的定量测量的潜力。它还为将社会意识纳入支助基础设施的设计开辟了广阔的前景。
英文摘要：
Efficient bug triaging procedures are an important precondition for successful collaborative software engineering projects. Triaging bugs can become a laborious task particularly in open source software (OSS) projects with a large base of comparably inexperienced part-time contributors. In this paper, we propose an efficient and practical method to identify valid bug reports which a) refer to an actual software bug, b) are not duplicates and c) contain enough information to be processed right away. Our classification is based on nine measures to quantify the social embeddedness of bug reporters in the collaboration network. We demonstrate its applicability in a case study, using a comprehensive data set of more than 700,000 bug reports obtained from the Bugzilla installation of four major OSS communities, for a period of more than ten years. For those projects that exhibit the lowest fraction of valid bug reports, we find that the bug reporters' position in the collaboration network is a strong indicator for the quality of bug reports. Based on this finding, we develop an automated classification scheme that can easily be integrated into bug tracking platforms and analyze its performance in the considered OSS communities. A support vector machine (SVM) to identify valid bug reports based on the nine measures yields a precision of up to 90.3% with an associated recall of 38.9%. With this, we significantly improve the results obtained in previous case studies for an automated early identification of bugs that are eventually fixed. Furthermore, our study highlights the potential of using quantitative measures of social organization in collaborative software engineering. It also opens a broad perspective for the integration of social awareness in the design of support infrastructures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
