Information entropy-based clustering algorithm for rapid software fault diagnosis

Yin Zhao Li*, Chang Zhen Hu, Kun Sheng Wang, Li Na Xu, Hui Ling He, Jia Dong Ren

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

In order to rapidly diagnose and locate the fault, we present ICARSFD (Information entropy-based Clustering Algorithm for Rapid Software Fault Diagnosis). In this paper, the average entropy and the total entropy are defined to guide the clustering operation over fault modes. This algorithm firstly stores the related information of existing faults in the form of fault tree, and deems each fault as an initial cluster. By calculating the information entropy between clusters and comparing them with the average entropy and the total entropy, fault clustering is completed. For the faults inappropriate to their located clusters, we take a retrospective approach to cluster them. Thereby the clustering effect related with the fault order could be addressed. Secondly, according to the ascending order of information entropy, the fault to be analyzed is matched to each cluster. Lastly, both the fault diagnosis results and fault paths are put out. In addition, if the fault match isn't successful, the fault path will be identified through fault tree, and the clustering results will be updated later. The experimental results demonstrate that ICARSFD has both good clustering effect and detection effect.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Machine Learning and Cybernetics
Pages2106-2111
Number of pages6
DOIs
Publication statusPublished - 2009
Event2009 International Conference on Machine Learning and Cybernetics - Baoding, China
Duration: 12 Jul 200915 Jul 2009

Publication series

NameProceedings of the 2009 International Conference on Machine Learning and Cybernetics
Volume4

Conference

Conference2009 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityBaoding
Period12/07/0915/07/09

Keywords

  • Cluster
  • Fault tree
  • Information entropy
  • Software security

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