A hierarchical mixture model for software reliability prediction

It is important to develop general prediction models in current software reliability research. In this paper, we propose a hierarchical mixture of software reliability models (HMSRM) for software reliability prediction. This is an application of the hierarchical mixtures of experts (HME) architecture. In HMSRM, individual software reliability models are used as experts. During the training of HMSRM, an Expectation-Maximizing (EM) algorithm is employed to estimate the parameters of the model. Experiments illustrate that our approach performs quite well in the later stages of software development, and better than single classical software reliability models. We show that the method can automatically select the most appropriate lower-level model for the data and performances are well in prediction.