Effective Bug Triage Based on a Hybrid Neural Network

Hongbing Wang, Qi Li
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引用次数: 3

Abstract

With the increasing scale and complexity of open source software, the quality of software has become a focus to which repairers pay close attention. Due to the inevitable existence of some known or unknown bugs in software,under certain conditions, software bugs may directly cause program running errors, and then produce abnormal running results and wrong program behavior, which will cause huge economic losses. Therefore, software defect repair is an important part of software evolution and quality assurance. Quickly and efficiently assigning defect reports to the right repairer for repair,to ensure efficiency and reduce the cost of open-source software development is an important problem that must be solved in software quality improvement. In this study, we propose a new defect report repair recommendation algorithm, RCNN, which can effectively learn the features of the defect report and recommend the appropriate repairer according to the feature. The proposed algorithm uses a CNN convolution kernel to capture the local information of the text and RNN is used to capture the sequence information of the text. The attention mechanism is introduced to learn the contribution ratio of each part of the text to the overall semantic information of the text. Thus, to a certain extent, it makes up for the defect that RNN cannot effectively learn and monitor remote information. Through experiments on the Eclipse and Mozilla datasets, compared with NB (naive Bayes), SVM (support vector machines), LeeCNN and DBRNNA, the RCNN model can effectively find the appropriate bug repairer among many repairers, and achieve higher classification accuracy.
基于混合神经网络的有效Bug分类
随着开源软件规模的不断扩大和复杂度的不断提高,软件的质量问题已经成为维修人员关注的焦点。由于软件中不可避免地存在一些已知或未知的bug,在一定条件下,软件bug可能直接导致程序运行错误,进而产生异常的运行结果和错误的程序行为,造成巨大的经济损失。因此,软件缺陷修复是软件进化和质量保证的重要组成部分。快速有效地将缺陷报告分配给合适的修复人员进行修复,以确保开源软件开发的效率和降低成本,是软件质量改进中必须解决的重要问题。在本研究中,我们提出了一种新的缺陷报告修复推荐算法RCNN,该算法可以有效地学习缺陷报告的特征,并根据特征推荐合适的修复人员。该算法使用CNN卷积核捕获文本的局部信息,使用RNN捕获文本的序列信息。引入注意机制,学习文本各部分对文本整体语义信息的贡献比例。从而在一定程度上弥补了RNN无法有效学习和监控远程信息的缺陷。通过在Eclipse和Mozilla数据集上的实验,与NB(朴素贝叶斯)、SVM(支持向量机)、LeeCNN和dbnna相比,RCNN模型可以有效地在众多修复器中找到合适的bug修复器,并达到更高的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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