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Park, J., Lee, N. and Baik, J. (2014) On the Long-Term Predictive Capability of Data-Driven Software Reliability Model: An Empirical Evaluation. Proceedings of the 25th International Symposium on Software Reliability Engineering, Naples, 3-6 November 2014, 45-54.

has been cited by the following article:

• TITLE: A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation

  AUTHORS: Momotaz Begum, Tadashi Dohi

  KEYWORDS: Software Reliability, Artificial Neural Network, Box-Cox Power Transformation, Long-Term Prediction, Fault Count Data, Empirical Validation

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.10 No.3, March 30, 2017

  ABSTRACT: Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron neural network, where the underlying software fault count data are transformed to the Gaussian data, by means of the well-known Box-Cox power transformation. More specially, we investigate the long-term behavior of software fault counts by the neural network, and perform the multi-stage look ahead prediction of the cumulative number of software faults detected in the future software testing. In numerical examples with two actual software fault data sets, we compare our neural network approach with the existing software reliability growth models based on nonhomogeneous Poisson process, in terms of predictive performance with average relative error, and show that the data transformation employed in this paper leads to an improvement in prediction accuracy.