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Revisiting the performance of mixtures of software reliability growth models

In this paper, we evaluate the performance of a mixture of software reliability growth models (Super models) against the original base models. The base models chosen are four well known nonhomogeneous Poisson process (NHPP) growth models proposed in literature. We use the method of maximum likelihood for fitting these models to data. The Super models are based on the above base models plus a selection criterion based on past prediction measures. Four Poisson type reliability models and four Super models are evaluated based on their performance on three data sets. The performance is judged by the relative error of the predicted number of failures over future time intervals relative to the number of failures eventually observed during the interval. Conclusions are drawn based on the analysis of the results and the use of the selection of models.