Software Reliability Prediction using Artificial Techniques
Due to the growth in demand for software with high reliability and safety, software reliability prediction becomes more and more essential. Software reliability is a key part of software quality. Over the years, many software reliability models have been successfully utilized in practical software reliability engineering, however, no single model can obtain accurate prediction for all cases. So in order to improve the accuracy of software reliability prediction the proposed model combine the software reliability models with the neural networks (NN). Particle swarm optimization (PSO) algorithm has been chosen and applied for learning process to select the best architecture of the neural network. The applicability of the proposed model is demonstrated through three software failure data sets. The results show that the proposed model has good prediction capability and more applicable for software reliability prediction.
Keywords: software reliability prediction, neural network, particle swarm optimization
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ABOUT THE AUTHORS
Rita G. Al Gargoor
M.Sc. Student in Software Engineering Department, Mosul University, College of Computer sc. And Mathematics, Iraq
Nada N. Saleem
Dr. Nada N. Saleem , Assistant Professor in in Software Engineering Department, Mosul University, College of Computer sc. And Mathematics, Iraq
Rita G. Al Gargoor
M.Sc. Student in Software Engineering Department, Mosul University, College of Computer sc. And Mathematics, Iraq
Nada N. Saleem
Dr. Nada N. Saleem , Assistant Professor in in Software Engineering Department, Mosul University, College of Computer sc. And Mathematics, Iraq
