期刊
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
卷 27, 期 6, 页码 925-949出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218194017500346
关键词
Software engineering; bug localization; information retrieval; bug report
类别
资金
- Natural Science Foundation of Jiangsu Province [BK20151476]
- National Basic Research Program of China (973 Program) [2014CB744903]
- National High-Tech Research and Development Program of China (863 Program) [2015AA015303]
- Collaborative Innovation Center of Novel Software Technology and Industrialization
- Fundamental Research Funds for the Central Universities [NS2016093]
- EPSRC [EP/P00430X/1]
- National Natural Science Foundation of China [61662035]
- State Key Laboratory of Novel Software Technology, Nanjing University [KFKT2014A14]
- European CHIST-ERA
- EPSRC [EP/P00430X/2, EP/P00430X/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/P00430X/2, EP/P00430X/1] Funding Source: researchfish
Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source-file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of the approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e. ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据