Enhancing accuracy of software reliability prediction

The measurement and prediction of software reliability require the use of the software reliability growth models (SRGMs). The predictive quality can be measured by the average end-point projection error. In this paper, the effects of two orthogonal classes of approaches to improve prediction capability of a SRM have been examined using a large number of data sets. The first approach is preprocessing of data to filter out short term noise. The second is to overcome the bias inherent in the model. The results show that proper application of these two approaches can be more important than the selection of the model.