Neural Network Modeling for Software Reliability Prediction of Grouped Failure Data


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 7, No. 12, pp. 3821-3828, Dec. 2000
10.3745/KIPSTE.2000.7.12.3821,   PDF Download:

Abstract

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 that is able to predict cumulative failures in the variable future time for grouped failure data. A NN''s predictive ability can be affected by what it learns and in its learning sequence. Eleven training regimes that represents the input-output of NN are considered. The best training regimes are selected based on the next-step average relative prediction error (AE) and normalized AE (NAE). The suggested NN models are compared with other well-known NN models and statistical software reliability growth models (SRGMs) in order to evaluate performance. Experimental results show that the NN model with variable time interval information is necessary in order to predict cumulative failures in the variable future time interval.


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Cite this article
[IEEE Style]
S. U. Lee, Y. M. Park, S. J. Park, J. H. Park, "Neural Network Modeling for Software Reliability Prediction of Grouped Failure Data," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 12, pp. 3821-3828, 2000. DOI: 10.3745/KIPSTE.2000.7.12.3821.

[ACM Style]
Sang Un Lee, Yeong Mok Park, Soo Jin Park, and Jae Heung Park. 2000. Neural Network Modeling for Software Reliability Prediction of Grouped Failure Data. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 12, (2000), 3821-3828. DOI: 10.3745/KIPSTE.2000.7.12.3821.