Momotaz Begum
Department of Information Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima 739-8527 Japan.

Tadashi Dohi
Department of Information Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima 739-8527 Japan.

DOI https://dx.doi.org/10.33889/IJMEMS.2018.3.2-014

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Abstract

The determination of the software release time for a new software product is the most critical issue for designing and controlling software development processes. This paper presents an innovative technique to predict the optimal software release time using a neural network. In our approach, a three-layer perceptron neural network with multiple outputs is used, where the underlying software fault count data are transformed into the Gaussian data by means of the well-known Box-Cox power transformation. Then the prediction of the optimal software release time, which minimizes the expected software cost, is carried out using the neural network. Numerical examples with four actual software fault count data sets are presented, where we compare our approach with conventional Non-Homogeneous Poisson Process (NHPP) -based Software Reliability Growth Models (SRGMs).

Keywords- Software cost model, Optimal software release time, Software reliability, Artificial neural network, Data transformation, Long-term prediction, Fault count data, Empirical validation.

Citation

Begum, M., & Dohi, T. (2018). Optimal Release Time Estimation of Software System using Box-Cox Transformation and Neural Network. International Journal of Mathematical, Engineering and Management Sciences, 3(2), 177-194. https://dx.doi.org/10.33889/IJMEMS.2018.3.2-014.