An Alternative Boosting-based Software Reliability Prediction Method

Although a huge number of software reliability models (SRMs) have been proposed in the past literature, there is no unique SRM with satisfactory prediction accuracy, because it has been known that the best goodness-of-fit SRM to the underlying data is not always equivalent to the best prediction model for the future software fault-count process. 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). In this paper we focus on a prediction SRM consisting with linearly weighted combinational (LWC) non-homogeneous Poisson process (NHPP)-based SRMs, and overview an AdaBoosting-based approach by Li et al. (2012) to determine the optimal weights for the LWC NHPP-based SRMs. Next, we propose a refined AdaBoosting technique to keep the steps of original AdaBoosting, in order to present the original predictive performance of AdaBoosting in software reliability.