Towards More Accurate Multi-Label Software Behavior Learning
Conference Proceeding Article
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%.
Computer Sciences | Software Engineering
Software and Cyber-Physical Systems
2014 Software Evolution Week: IEEE Conference on Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE): Proceedings: February 3-6, 2014, Antwerp
City or Country
XIA, Xin; YANG, Feng; LO, David; CHEN, Zhenyu; and WANG, Xinyu.
Towards More Accurate Multi-Label Software Behavior Learning. (2014). 2014 Software Evolution Week: IEEE Conference on Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE): Proceedings: February 3-6, 2014, Antwerp. 134-143. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2032