Combination of Predictions Obtained from Different Software Reliability Growth Models

In the development of software reliability measurement and prediction, many software reliability growth models have been proposed. Application of these models to real data sources has shown that there is commonly great disagreement in predictions, while none of them has been shown to be more trustworthy than others in terms of predictive quality in all applications. In this paper, we shall use various methods to get new predictions by combining the predictions obtained from different models. Two weight decision approaches which we call Bayesian Inference and Switching have been used in this combination. We shall show the resulting predictive quality by application of these techniques to a number of data sets. By using the combined prediction method we can get improved predictions or automatically choose the "best" prediction system from all the available prediction systems. The combination method has no specific requirements on the prediction systems being combined.