基于循环神经网络的缺陷报告分派方法
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席圣渠(1992-),男,吉林榆树人,博士生,主要研究领域为软件智能化开发技术与方法;徐锋(1975-),男,博士,教授,博士生导师,CCF专业会员,主要研究领域为可信软件,软件智能化开发技术与方法;姚远(1987-),男,博士,助理研究员,CCF专业会员,主要研究领域为数据驱动的软件智能化;吕建(1960-),男,博士,教授,博士生导师,CCF会士,主要研究领域为形式化方法,中间件技术,Agent技术,分布式对象技术

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席圣渠,E-mail:nju.cellzero@gmail.com

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基金项目:

国家重点研发计划(2016YFB1000802);国家自然科学基金(61702252,61672274)


Bug Triaging Approach Based on Recurrent Neural Networks
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National Key Research and Development Program of China (2016YFB1000802); National Natural Science Foundation of China (61702252, 61672274)

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    摘要:

    随着开源软件项目规模的不断增大,人工为缺陷报告分派合适的开发人员(缺陷分派)变得越来越困难.而不合适的缺陷分派往往会严重影响缺陷修复的效率,为此,迫切需要一种缺陷分派辅助技术帮助项目管理者更好地完成缺陷分派任务.当前,大部分研究工作都基于缺陷报告文本以及相关元数据信息分析来刻画开发者的特征,忽略了对开发者活跃度的考虑,使得对具有相似特征的开发者进行缺陷报告分派预测时表现较差.提出一个基于循环神经网络的深度学习模型DeepTriage,一方面,利用双向循环网络加池化方法提取缺陷报告的文本特征;另一方面,利用单向循环网络提取特定时刻的开发者活跃度特征,并融合两者,利用已修复的缺陷报告进行监督学习.在Eclipse等4个不同的开源项目数据集上的实验结果表明,DeepTriage较之同类工作在缺陷分派预测准确率上有显著提升.

    Abstract:

    With the increasing size of open source software projects, assigning suitable developers for bug reports (i.e., bug triaging) is becoming more and more difficult. Moreover, the efficiency of bug repairing will likely be reduced if the bugs are assigned to inappropriate developers. Therefore, it is necessary to provide an automatic bug triaging technique for the project managers to better assign bug reports. Existing work for this task mainly focuses on analyzing the text and metadata in bug reports to characterize the relationships between developers and bug reports, while the active level of developers is largely ignored. A shortcoming of these methods is that they may lead to poor performance when developers with different active levels have similar characteristics. This paper proposes a learning model named DeepTriage based on the recurrent neural networks. On the one hand, the ordered natural language text in bug reports is mapped into high-level features by a bidirectional RNN. On the other hand, developer's active level is extracted and transformed into high-level features through a single directional RNN. Then, the features of text and developer's active level are combined and learned from bug reports with known fixers. Experimental results on four different open-source data sets (e.g., Eclipse) show that DeepTriage has significantly improved the accuracy of bug triaging compared with existing work.

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席圣渠,姚远,徐锋,吕建.基于循环神经网络的缺陷报告分派方法.软件学报,2018,29(8):2322-2335

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历史
  • 收稿日期:2017-07-18
  • 最后修改日期:2017-09-28
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  • 在线发布日期: 2018-03-13
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