Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Software Reliability Assessment with Fuzzy Least Squares Support Vector Machine Regression

  • Hwang, Chang-Ha (Department of Statistical Information, Catholic University of Daegu) ;
  • Hong, Dug-Hun (School of Mechanical and Automotive Engineering, Catholic University of Daegu) ;
  • Kim, Jang-Han (Department of Statistics, Keimyung University)
  • Published : 2003.08.01
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Abstract

Software qualify models can predict the risk of faults in the software early enough for cost-effective prevention of problems. This paper introduces a least squares support vector machine (LS-SVM) as a fuzzy regression method for predicting fault ranges in the software under development. This LS-SVM deals with the fuzzy data with crisp inputs and fuzzy output. Predicting the exact number of bugs in software is often not necessary. This LS-SVM can predict the interval that the number of faults of the program at each session falls into with a certain possibility. A case study on software reliability problem is used to illustrate the usefulness of this LS -SVM.

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References

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