Improving software reliability estimation using multi-layer neural-network combination model. (2017)

• Record Type: Journal Article
• Authors: Lakshmanan, Indhurani; Ramasamy, Subburaj
• Is Part Of: International journal of innovative computing and applications. Volume 8:Number 2(2017)
• Page Start: 113
• Page End: 121
• Publication Date: 2017
• Subjects: software reliability growth models -- SRGM -- artificial neural network -- ANN -- software reliability estimation -- the learning phenomenon of the testing team -- generalised NHPP model; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

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.