ANALYSIS OF SOME COMPUTATIONAL INTELLIGENCE APPROACHES FOR SOFTWARE RELIABILITY PREDICTION

Abstract

Software engineering is partial without Software reliability prediction. “For characterizing any software product quality quantitatively during phase of testing, the most important factor is software reliability assessment. Traditional models are mainly based on assumptions and approximations. But it is needed for developing such a single model which can be applicable for a relatively better prediction in all conditions and situations. For this the Neural Network (NN) model approach is introduced. In this thesis report the applicability of the models based on NN for better reliability prediction in a real environment is described and a method of assessment of growth of software reliability using NN model is presented. Mainly three types of NNs are used here. One is feed forward neural network, second is generalized regression neural network and third is radial basis function network. For modeling FFNN, back propagation learning algorithm is implemented and the related network architecture issues, data representation methods and some unreal assumptions associated with software reliability models are discussed. Different datasets containing software failures are applied to the proposed models. These datasets are obtained from several software projects. Then it is observed that the results obtained indicate a significant improvement in performance by using neural network models over conventional statistical models based on non homogeneous Poisson process.”