2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE) (2014)
Feb. 3, 2014 to Feb. 6, 2014
Xin Xia , College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Yang Feng , State Key Laboratory for Novel Sofware Technology, Nanjing University, Nanjing, China
David Lo , School of Information Systems, Singapore Management University, Singapore
Zhenyu Chen , State Key Laboratory for Novel Sofware Technology, Nanjing University, Nanjing, China
Xinyu Wang , College of Computer Science and Technology, Zhejiang University, Hangzhou, China
In a modern software system, when a program fails, a crash report which contains an execution trace would be sent to the software vendor for diagnosis. A crash report which corresponds to a failure could be caused by multiple types of faults simultaneously. Many large companies such as Baidu organize a team to analyze these failures, and classify them into multiple labels (i.e., multiple types of faults). However, it would be time-consuming and difficult for developers to manually analyze these failures and come out with appropriate fault labels. In this paper, we automatically classify a failure into multiple types of faults, using a composite algorithm named MLL-GA, which combines various multi-label learning algorithms by leveraging genetic algorithm (GA). To evaluate the effectiveness of MLL-GA, we perform experiments on 6 open source programs and show that MLL-GA could achieve average F-measures of 0.6078 to 0.8665. We also compare our algorithm with Ml.KNN and show that on average across the 6 datasets, MLL-GA improves the average F-measure of MI.KNN by 14.43%.
Software algorithms, Prediction algorithms, Genetic algorithms, Software, Computer crashes, Training, Biological cells
X. Xia, Y. Feng, D. Lo, Z. Chen and X. Wang, "Towards more accurate multi-label software behavior learning," 2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE), Antwerp, Belgium, 2014, pp. 134-143.