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Improving Bug Localization with an Enhanced Convolutional Neural Network

机译：通过增强的卷积神经网络改善错误定位

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Background: Localizing buggy files automatically speeds up the process of bug fixing so as to improve the efficiency and productivity of software quality teams. There are other useful semantic information available in bug reports and source code, but are mostly underutilized by existing bug localization approaches. Aims: We propose DeepLocator, a novel deep learning based model to improve the performance of bug localization by making full use of semantic information. Method: DeepLocator is composed of an enhanced CNN (Convolutional Neural Network) proposed in this study considering bug-fixing experience, together with a new rTF-IDuF method and pretrained word2vec technique. DeepLocator is then evaluated on over 18,500 bug reports extracted from AspectJ, Eclipse, JDT, SWT and Tomcat projects. Results: The experimental results show that DeepLocator achieves 9.77% to 26.65% higher Fmeasure than the conventional CNN and 3.8% higher MAP than a state-of-the-art method HyLoc using less computation time. Conclusion: DeepLocator is capable of automatically connecting bug reports to the corresponding buggy files and successfully achieves better performance based on a deep understanding of semantics in bug reports and source code.
机译：背景：对漏洞文件进行本地化会自动加快错误修复的过程，从而提高软件质量团队的效率和生产力。错误报告和源代码中还有其他有用的语义信息，但是大多数现有的错误本地化方法未充分利用这些语义信息。目的：我们提出了DeepLo​​cator，这是一个新颖的基于深度学习的模型，可通过充分利用语义信息来提高错误定位的性能。方法：DeepLo​​cator由本研究中提出的，考虑到错误修复经验的增强型CNN（卷积神经网络），新的rTF-IDuF方法和预训练的word2vec技术组成。然后根据从AspectJ，Eclipse，JDT，SWT和Tomcat项目中提取的18,500多个错误报告对DeepLo​​cator进行评估。结果：实验结果表明，DeepLo​​cator的Fmeasure比传统CNN高9.77％至26.65％，MAP比最新方法HyLoc高3.8％，而运算时间却更少。结论：DeepLo​​cator能够自动将错误报告连接到相应的错误文件，并且基于对错误报告和源代码中语义的深刻理解，可以成功地实现更好的性能。

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