Comparison of Software Reliability Growth Models by using AdaBoosting Algorithm
Vamsidhar Yendapalli, P Samba, S. Srinadh Raju · 2011
Software Reliability Growth Models (SRGMs) are very important for estimating and predicting software reliability. Several combinational methods of SRGMs have been proposed to improve the reliability estimation and prediction accuracy. The AdaBoosting (Adaptive Boosting) algorithm is one of the most popular machine learning algorithms. An AdaBoosting based approach for obtaining a dynamic weighted linear Combinational Model (ACM) is already proposed. The key idea of this approach is that we select several SRGMs as the weak predictors and use AdaBoosting algorithm to determine the weights of these models for obtaining the final linear combinational model. In this Paper 1. Investigating the fitting and prediction performance of the ACM by using maximum likelihood estimation to estimate the parameters of models and also Comparing fitting and prediction performance of SRGMs and ACM with real failure data-sets. 2. Comparison of each selected individual model with ACM on the basis of Time Complexity.