作者
Pratik Roy, GS Mahapatra, Pooja Rani, SK Pandey, Kashi Nath Dey
发表日期
2014/9/1
期刊
Applied Soft Computing
卷号
22
页码范围
629-637
出版商
Elsevier
简介
Traditional parametric software reliability growth models (SRGMs) are based on some assumptions or distributions and none such single model can produce accurate prediction results in all circumstances. Non-parametric models like the artificial neural network (ANN) based models can predict software reliability based on only fault history data without any assumptions. In this paper, initially we propose a robust feedforward neural network (FFNN) based dynamic weighted combination model (PFFNNDWCM) for software reliability prediction. Four well-known traditional SRGMs are combined based on the dynamically evaluated weights determined by the learning algorithm of the proposed FFNN. Based on this proposed FFNN architecture, we also propose a robust recurrent neural network (RNN) based dynamic weighted combination model (PRNNDWCM) to predict the software reliability more justifiably. A real …
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P Roy, GS Mahapatra, P Rani, SK Pandey, KN Dey - Applied Soft Computing, 2014