Abstract

Software Reliability is a key concern of many users and developers of software. Demand for high software reliability requires robust modeling techniques for software quality prediction. Software reliability models are very useful to estimate the probability of the software fail along the time. Several different models have been proposed to predict the software reliability growth model (SRGM); however, none of them has proven to perform well considering different project characteristics. The ability to predict the number of faults in the software during development and testing processes. In this paper, we explore connectionist artificial neural networks models as an alternative approach to derive these models by investigating the performance analysis of four different connectionist paradigms for modeling the software reliability prediction. The presented four paradigms are multi-layer perceptron neural network, radial-basis functions, Elman recurrent neural networks and a Takagi-Sugeno fuzzy inference system learned using a neural network algorithm (neuro-fuzzy model). The results show that the neural network model adopted has good predictive capability.

Keywords: Neural Network, Software Quality, Software Reliability, and Time-Series Prediction

(P1120827001, 1.12 MB)

Biographies:

Sultan Hamadi Aljahdali, Ph.D. received the B.S from Winona State University, Winona, Minnesota in 1992, and M.S. with honor from Minnesota State University, Mankato, Minnesota, 1996, and Ph.D. Information Technology from George Mason University, Fairfax, Virginia, U.S.A, 2003. He is an associate dean of the college of computers and information systems at Taif University. His research interest includes software testing, developing software reliability models, soft computing for software engineering, computer security, reverse engineering, and medical imaging, also he is a member of ACM, IEEE, and ISCA.

Khalid A. Buragga is an Assistant Professor of Information Systems department in the college of Computer Sciences and Information Technology at King Faisal University, Hofuf, Saudi Arabia. He received his B.Sc. in Computer Information Systems from King Faisal University. And, he received his M.Sc. in Computer Information Systems from University of Miami, USA, and a Ph.D. in Information Technology from George Mason University, USA. His research interests include Software Design, Software Development, Software Quality, Software Reliability, E-Commerce and Web development, Business Process Re-engineering, and Integrating Systems.