Refined Software Reliability Prediction: A Bagging Approacha

Over the past few decades, a huge number of software reliability models (SRMs) have been proposed in the literature. Then, it is common to select an appropriate SRM with highest goodness-of-fit to the underlying software fault-count data based on any information criteria such as Akaike Information Criterion (AIC). However, it has been known that the best SRM with the minimum AIC is not always equivalent to the best prediction model for the future software fault-count process. In this paper, we propose refined software reliability prediction methods with a bagging-based ranking and model averaging technique. In numerical experiments with actual software development project data, it is shown that our bagging-based software reliability prediction models enabled to improve the predictive performances in the early and middle software testing phases, comparing with the single use of the best SRM with the minimum AIC.