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An Empirical Study on Bug Assignment Automation Using Chinese Bug Data

机译:使用汉语BUG数据进行错误分配自动化的实证研究

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Bug assignment is an important step in bug life-cycle management. In large projects, this task would consume a substantial amount of human effort. To compare with the previous studies on automatic bug assignment in FOSS (Free/Open Source Software) projects, we conduct a case study on a proprietary software project in China. Our study consists of two experiments of automatic bug assignment, using Chinese text and the other non-text information of bug data respectively. Based on text data of the bug repository, the first experiment uses SVM to predict bug assignments and achieve accuracy close to that by human triagers. The second one explores the usefulness of non-text data in making such prediction. The main results from our study includes that text data are most useful data in the bug tracking system to triage bugs, and automation based on text data could effectively reduce the manual effort.
机译:错误分配是Bug Life-Cycle管理的重要一步。在大型项目中,这项任务将消耗大量的人类努力。要与先前的硕士(免费/开源软件)项目中的自动错误分配进行比较,我们开展了中国专有软件项目的案例研究。我们的研究包括两个自动错误分配的实验,分别使用中文文本和其他非文本信息。基于BUG存储库的文本数据,第一个实验使用SVM来预测BUG分配,并通过人类交联者实现靠近该的精度。第二个探讨非文本数据在制定这种预测方面的有用性。我们研究的主要结果包括该文本数据在错误跟踪系统中的最有用数据到分类错误,并且基于文本数据的自动化可以有效地减少手动努力。

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