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Towards More Accurate Multi-Label Software Behavior Learning

Country : Singapore
Department : Singapore Management University
Project Title : Towards More Accurate Multi-Label Software Behavior Learning
Researcher : LO, David , CHEN, Zhenyu , WANG, Xinyu , XIA, Xin , YANG, Feng
Keyword : Computer Sciences , Software Engineering
Publisher : Institutional Knowledge at Singapore Management University
Year End : 2014
Identifier : https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3031&context=sis_research , https://ink.library.smu.edu.sg/sis_research/2032
Source : Research Collection School Of Computing and Information Systems
Abstract / Description :

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%.

References

LO, David and others / et al. (2014). Towards More Accurate Multi-Label Software Behavior Learning.  Singapore: Singapore Management University.
LO, David and others / et al. 2014. "Towards More Accurate Multi-Label Software Behavior Learning".  Singapore: Singapore Management University.
LO, David and others / et al. "Towards More Accurate Multi-Label Software Behavior Learning."  Singapore: Singapore Management University, 2014. Print.
LO, David and others / et al. Towards More Accurate Multi-Label Software Behavior Learning. Singapore: Singapore Management University; 2014.

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