ON THE USE AND THE PERFORMANCE OF SOFTWARE-RELIABILITY GROWTH-MODELS

We address the problem of predicting future failures for a piece of software. The number of failures occurring during a finite future time interval is predicted from the number of failures observed during an initial period of usage by using software reliability growth models. Two different methods for using the models are considered: straightforward use of individual models (simple models), and dynamic selection among models based on goodness-of-fit and quality-of-prediction criteria (super models). Performance is judged by the relative error of the predicted number of failures over future finite time intervals relative to the number of failures eventually observed during the intervals. Six simple models and eight super models are evaluated based on their performance on twenty data sets. This study is by no means comprehensive. Some conclusions can be drawn, but many open questions remain regarding the use and the performance of software reliability growth models.