Towards more accurate multi-label software behavior learning

Xin Xia, Yang Feng, David Lo, Zhenyu Chen, Xinyu Wang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

32 Citations (Scopus)

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%.

Original languageEnglish
Title of host publicationIEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering 2014 Proceedings
Subtitle of host publicationAntwerp, Belgium 3-6 February 2014
EditorsSerge Demeyer, Dave Binkley, Filippo Ricca
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages134-143
Number of pages10
ISBN (Electronic)9781479937523
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventIEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering 2014 - Antwerp, Belgium
Duration: 3 Feb 20146 Feb 2014
Conference number: 1st
http://ansymo.ua.ac.be/csmr-wcre

Conference

ConferenceIEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering 2014
Abbreviated titleCSMR-WCRE 2014
CountryBelgium
CityAntwerp
Period3/02/146/02/14
Internet address

Keywords

  • Genetic Algorithm
  • Multi-label Learning
  • Software Behavior Learning

Cite this