Paper Details: Downloads: 473
Serial Number: P1120827001
Title: Employing four ANNs Paradigms for Software Reliability Prediction: an Analytical Study
Authors: Sultan H. Aljahdali and Khalid A. Buragga.
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
Journal/Conference: International Journal of Artificial Intelligence and Machine Learning
Volume: 8
Issue: 2
Submission Date: 6/29/2008 12:00:00 AM
Review Date: 6/30/2008 12:00:00 AM
Publishing Date: 7/10/2008 12:00:00 AM
Article Downloads: 473
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