کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495381 862825 2014 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction
ترجمه فارسی عنوان
مدل های ترکیبی وزن پویا با استفاده از شبکه های عصبی ثابت و قوی برای پیش بینی قابلیت اطمینان نرم افزار
کلمات کلیدی
مدل رشد قابلیت اطمینان نرم افزار، مدل ترکیبی وزن دینامیک، شبکه های عصبی مصنوعی، الگوریتم ژنتیک، پیش بینی قابلیت اطمینان
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• We propose robust feedforward and recurrent neural network based dynamic weighted combination models.
• We combine four software reliability growth models by dynamically evaluated weights.
• We propose genetic algorithm based learning algorithm to train the proposed ANNs.
• Experimental results demonstrate that proposed models have fairly accurate predictability.
• PRNNDWCM has best software reliability prediction capability.

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-coded genetic algorithm (GA) is proposed to train the ANNs. Predictability of the proposed models are compared with the existing ANN based software reliability models through three real software failure data sets. We also compare the performances of the proposed models with the models that can be developed by combining three or two of the four SRGMs. Comparative studies demonstrate that the PFFNNDWCM and PRNNDWCM present fairly accurate fitting and predictive capability than the other existing ANN based models. Numerical and graphical explanations show that PRNNDWCM is promising for software reliability prediction since its fitting and prediction error is much less relative to the PFFNNDWCM.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 22, September 2014, Pages 629–637
نویسندگان
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