Adaboosting-based dynamic weighted combination of software reliability growth models

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Abstract

Software reliability growth models (SRGMs) are very important for software reliability estimation and prediction and have been successfully applied in the critical airborne software. However, there is no general model which can perform well for different cases. Thus, some researchers proposed to obtain more accurate estimation and prediction than one single model by combining various individual SRGMs together. AdaBoosting is a commonly used machine learning algorithm for combining several weak predictors into a single strong predictor to significantly improve the estimating and forecasting accuracy, which may be very suitable for the combination of SRGMs. Hence, two novel AdaBoosting-based combination approaches for improving the parametric SRGMs are presented in this paper. The first one selects several variations of one original SRGM for obtaining the self-combination model (ASCM). The second selects several various candidate SRGMs for obtaining the multi-combinational model (AMCM). Finally, two case studies are presented and the results show that: (1) the ASCM is fairly effective and applicable for improving the estimation and prediction performance of its corresponding original SRGM without adding any other factors and assumptions; (2) the AMCM is notably effective and applicable for combining SRGMs because it has well applicability and provides a significantly better reliability estimation and prediction power than the traditional SRGMs and also yields a better estimation and prediction power than the neural-network-based combinational model. Copyright © 2011 John Wiley & Sons, Ltd.

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