A Study of the Connectionist Models for Software Reliability Prediction

When analysing software failure data, many software reliability models are available and in particular, nonhomogeneous Poisson process (NHPP) models are commonly used. However, difficulties posed by the assumptions, their validity, and relevance of these assumptions to the real testing environment have limited their usefulness. The connectionist approach using neural network models are more flexible and with less restrictive assumptions. This model-free technique requires only the failure history as inputs and then develops its own internal model of failure process. Their ability to model nonlinear patterns and learn from the data makes it a valuable alternative methodology for characterising the failure process. In this paper, a modified Elman recurrent neural network in modeling and predicting software failures is investigated. The effects of different feedback weights in the proposed model are also studied. A comparative study between the proposed recurrent architecture, with the more popular feedforward neural network, the Jordan recurrent model, and some traditional parametric software reliability growth models are carried out.