[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5391/JKIIS.2003.13.4.486

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)

Publication Information

Journal of the Korean Institute of Intelligent Systems / v.13, no.4, 2003 , pp. 486-490 More about this Journal

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.

Keywords

Least squares support vector machine (LS-SVM); Triangular membership function; Software reliability;

Citations & Related Records

Reference

1	J. A. K. Suykens and J. Vandewalle, "Recurrent least squares support vector machines," IEEE Transactions on Circuits and Systems-I, Vol. 47, No. 7, pp. 1109-1114, 2000. DOI ScienceOn
2	D. H. Hong and C. Hwang, "Support vector fuzzy regression machines," to be appeared in Fuzzy Sets and Systems, 2003.
3	P. D'Urso and T. Gastaldi, "An orderwise polynomial regression procedure for fuzzy data," Fuzzy Sets and Systems, Vol. 130, pp. 1-19, 2002. DOI ScienceOn
4	J. A. K. Suykens, "Nonlinear modelling and support vector machines," Proc. of the IEEE International Conference on Instrumentation and Measurement Technology, pp. 287-294, 2001.
5	M. J. L. Orr, "Introduction to radial basis function networks," Centre for Cognitive Science Technical Report, U. of Edinburgh, 1996.
6	V. N. Vapnik, "Statistical Learning Theory," John Wiley & Sons, New York, 1998.
7	Z. Xu, T. M. Khoshgoftaar and E. B. AlIen, "Prediction of software faults using fuzzy nonlinear regression modeling," In Proceedings: Fifth IEEE International Symposium on High-Assurance Systems Engineering, Albuquerque, New Mexico USA, November 2000. IEEE Computer Society.
8	T. M. Khoshgoftaar, E. B. AlIen, R. Halstead and G. P. Trio, "Detection of fault-prone software modules during a spiral life cycle," In Proceedings of the International Conference on Software Maintenance, pp. 69-76, Monterey, CA, November 1996. IEEE Computer Society.