Improving software reliability estimation using multi-layer neural-network combination model
Indhurani Lakshmanan, Subburaj Ramasamy · International Journal of Innovative Computing and Applications · 2017
Software reliability growth models (SRGM) can be used to derive quantitative information about the quality of the product and also to estimate the software testing time needed to achieve target reliability. The modelling performance of SRGMs depends on the nature of the failure data set. In this paper, we propose a multi-layer feed-forward artificial neural network (ANN) combination model using two widely used generalised SRGMs as base models with back-propagation training algorithm. The proposed ANN-based model is compared with the traditional SRGMs and a few ANN based software reliability models. The performance comparison seems to confirm that, the goodness of fit and the predictive validity of proposed model appears to be better than that of reliability models compared. The proposed ANN-based model provides consistent performance for both exponential, and S-shaped growth of mean value function witnessed in software projects.