Authors
Yan Xiao, Jacky Keung, Qing Mi, Kwabena E Bennin
Publication date
2017/12/4
Conference
2017 24th Asia-Pacific Software Engineering Conference (APSEC)
Pages
338-347
Publisher
IEEE
Description
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 …
Total citations
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Scholar articles
Y Xiao, J Keung, Q Mi, KE Bennin - 2017 24th Asia-Pacific Software Engineering …, 2017