Prediction of Software Reliability Using Neural Network & Fuzzy Logic : Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability

Sultan Aljahdali

ISBN 10: 3639363590 / ISBN 13: 9783639363593

Published by VDM Verlag Dr. Müller, 2011

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Developing a reliable software system, several issues need to be addressed. These issues include the definition of reliable software, reliable development methodologies, testing methods for reliability, and reliability growth prediction modeling. Many software reliability growth models were proposed with the goal to estimate the number of residual software faults, which occur in the software testing process. In this thesis, we explore an alternative approach using two types of neural networks (NN) models, the feedforward and the Radial basis function. We also use of fuzzy rules. NNs have been used both to estimate parameters of a formal model and to learn to emulate the process model itself to predict future faults. Feedforward and Radial basis function have been successfully used to solve a variety of prediction problems, which include real-time control, military, and operating system applications. A set of fuzzy rules were also developed to model the dynamics of the software reliability growth models in various applications. The reported results using neural networks and fuzzy logic can improve the software reliability growth modeling solution.

Seller Inventory # 9783639363593

Bibliographic Details

• Title: Prediction of Software Reliability Using Neural Network & Fuzzy Logic : Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2011
• Language: English
• ISBN 10: 3639363590
• ISBN 13: 9783639363593
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 185 grams

About this title

Synopsis

Developing a reliable software system, several issues need to be addressed. These issues include the definition of reliable software, reliable development methodologies, testing methods for reliability, and reliability growth prediction modeling. Many software reliability growth models were proposed with the goal to estimate the number of residual software faults, which occur in the software testing process. In this thesis, we explore an alternative approach using two types of neural networks (NN) models, the feedforward and the Radial basis function. We also use of fuzzy rules. NNs have been used both to estimate parameters of a formal model and to learn to emulate the process model itself to predict future faults. Feedforward and Radial basis function have been successfully used to solve a variety of prediction problems, which include real-time control, military, and operating system applications. A set of fuzzy rules were also developed to model the dynamics of the software reliability growth models in various applications. The reported results using neural networks and fuzzy logic can improve the software reliability growth modeling solution.

About the Author

Received Ph.D in Information Technology from George Mason University, USA in 2003. Currently Dr. Aljahdali is Associate Professor and Dean of the college of computers and information technology at Taif University. His research interests include software reliability models, soft computing for software engineering, and reverse engineering.