Improving Bug Localization with an Enhanced Convolutional Neural Network
Yan Xiao, Jacky Keung, Qing Mi, Kwabena Ebo Bennin · 2017
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.