Title

Towards More Accurate Multi-Label Software Behavior Learning

Publication Type

Conference Proceeding Article

Publication Date

2-2014

Abstract

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%.

Discipline

Computer Sciences | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2014 Software Evolution Week: IEEE Conference on Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE): Proceedings: February 3-6, 2014, Antwerp

First Page

134

Last Page

143

Identifier

10.1109/CSMR-WCRE.2014.6747163

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://dx.doi.org/10.1109/CSMR-WCRE.2014.6747163