Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging. (26th April 2018) - Rani, Pooja; Mahapatra, G.S.
Summary: This paper propose a learning algorithm of supervised back‐propagation neural networks for dynamic weighted combination of software reliability model. The proposed model is an assimilation of 3 well‐known non‐homogeneous poisson process (NHPP)–based software reliability growth models with imperfect debugging. The novel approach of proposed supervised back propagation–based neural network 2‐stage architecture has a great impact on the network by combining the imperfect debugging models based on the nature of fault introduction rate during testing and debugging. Function approximation metrics are used for comparing the proposed model with individual models. Three data sets are trained using supervised back‐propagation neural networks to compare the performance and validity evaluation of proposed and existing NHPP models and dynamic weighted combinational model. Reliability analysis among important NHPP models incorporating imperfect debugging is illustrated through numerical and graphical explanation of several metrics using supervised back‐propagation neural networks. Abstract : The novelty of this work is to give more attention on fault introduction rate in imperfect debugging during testing and debugging. It is a 2‐stage potential of new insight in DWCM approach in ANN in presence of backpropogation algorithm in software reliability fitting and modelling. This approach will eventually offer significant results than existing models.