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.

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.

Wavelet shrinkage estimation is a non-parametric technique to estimate the non-homogeneous Poisson process (NHPP)-based software reliability growth model (SRGM), and provides better goodness-of-fit performances than the common parametric approach by means of the maximum likelihood estimation. However, it has a serious drawback that not only the long-term prediction but also the quantification of software reliability for an arbitrary testing/operational period were difficult. In this paper we propose a long-term prediction approach for the NHPP-based SRGM with the wavelet shrinkage estimation, where several denoising and data transform techniques are applied to estimate the NHPP discrete intensity function. We develop a wavelet-based software reliability assessment tool; WSRAT, to enable a lightweight and automatic software reliability prediction, in addition to implement the existing denoising and data transform techniques. W-SRAT is a unique software reliability assessment tool based on the wavelet shrinkage estimation, and is a web-based freeware to predict the cumulative number of software faults and the quantitative software reliability. We demonstrate how to use the W-SRAT in actual software reliability management.