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Prediction of Software Reliability Using Neural Network & Fuzzy Logic: Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability - Softcover
Inhaltsangabe
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.
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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.
Biografía del autor
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.
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- VerlagVDM Verlag Dr. Müller
- Erscheinungsdatum2011
- ISBN 10 3639363590
- ISBN 13 9783639363593
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten112
- Kontakt zum HerstellerNicht verfügbar
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Prediction of Software Reliability Using Neural Network & Fuzzy Logic
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VDM Verlag Dr. Müller, 2011
ISBN 10: 3639363590
ISBN 13: 9783639363593
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Kartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Aljahdali SultanReceived 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. Bestandsnummer des Verkäufers 4981100
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Prediction of Software Reliability Using Neural Network & Fuzzy Logic : Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability
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Taschenbuch. Zustand: Neu. 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. Bestandsnummer des Verkäufers 9783639363593
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Prediction of Software Reliability Using Neural Network Fuzzy Logic Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability
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Prediction of Software Reliability Using Neural Network & Fuzzy Logic: Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability
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Prediction of Software Reliability Using Neural Network Fuzzy Logic Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability
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Prediction of Software Reliability Using Neural Network & Fuzzy Logic: Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability
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Prediction of Software Reliability Using Neural Network & Fuzzy Logic: Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability
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VDM Verlag Dr. M�ller 2011-06-10, 2011
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ISBN 13: 9783639363593
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Prediction of Software Reliability Using Neural Network & Fuzzy Logic: Using Neural Network and Fuzzy Logic Techniques for Predicting Software Reliability
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