Software Reliability Prediction of Grouped Failure Data Using Variant Models of Cascade - Correlation Learning Algorithm 


Vol. 8,  No. 4, pp. 387-392, Aug.  2001
10.3745/KIPSTD.2001.8.4.387


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  Abstract

This Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling for grouped failure data that is able to predict cumulative failures in the variable future time. The two variant models of cascade-correlation learning (CasCor) algorithm are presented. Suggested models are compared with other well-known NN models and statistical software reliability growth models (SRGMs). Experimental results show that the suggested models show better predictability.

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[IEEE Style]

S. U. Lee and J. Y. Park, "Software Reliability Prediction of Grouped Failure Data Using Variant Models of Cascade - Correlation Learning Algorithm," The KIPS Transactions:PartD, vol. 8, no. 4, pp. 387-392, 2001. DOI: 10.3745/KIPSTD.2001.8.4.387.

[ACM Style]

Sang Un Lee and Joong Yang Park. 2001. Software Reliability Prediction of Grouped Failure Data Using Variant Models of Cascade - Correlation Learning Algorithm. The KIPS Transactions:PartD, 8, 4, (2001), 387-392. DOI: 10.3745/KIPSTD.2001.8.4.387.

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