Comparison of Software Reliability Growth Models by Using AdaBoosting Algorithm

Provided by: International Journal on Computer Science and Technology (IJCST)
Topic: Software
Format: PDF
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 is already proposed. The key idea of this approach is that the authors 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.

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