Neural Network for Software Reliability Prediction with Unnormalized Data 


Vol. 7,  No. 5, pp. 1419-1425, May  2000
10.3745/KIPSTE.2000.7.5.1419


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  Abstract

When we predict of software reliability, we can't know the testing stopping time and how many faults be residues in software (the maximum value of data) during the software testing process, therefore we assume the maximum value and the training result can be inaccuracy. In this paper, we present neural network approach for software reliability prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data.

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[IEEE Style]

S. U. Lee, "Neural Network for Software Reliability Prediction with Unnormalized Data," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 5, pp. 1419-1425, 2000. DOI: 10.3745/KIPSTE.2000.7.5.1419.

[ACM Style]

Sang Un Lee. 2000. Neural Network for Software Reliability Prediction with Unnormalized Data. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 5, (2000), 1419-1425. DOI: 10.3745/KIPSTE.2000.7.5.1419.

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