Sinopsis
There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. In this book, we use Bayesian modeling to determine the influential relationships among software metrics and defect proneness. Furthermore, we propose a novel technique for defect prediction that uses plagiarism detection tools. We use kernel programming to model the relationship between source code similarity and defectiveness and suggest that source code similarity is a good means of predicting both defectiveness and the number of defects in software systems.
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Reseña del editor
There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. In this book, we use Bayesian modeling to determine the influential relationships among software metrics and defect proneness. Furthermore, we propose a novel technique for defect prediction that uses plagiarism detection tools. We use kernel programming to model the relationship between source code similarity and defectiveness and suggest that source code similarity is a good means of predicting both defectiveness and the number of defects in software systems.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Software Defect Prediction using Bayesian Networks and Kernel Methods
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. In this book, we use Bayesian modeling to determine the influential relationships among software metrics and defect proneness. Furthermore, we propose a novel technique for defect prediction that uses plagiarism detection tools. We use kernel programming to model the relationship between source code similarity and defectiveness and suggest that source code similarity is a good means of predicting both defectiveness and the number of defects in software systems. 168 pp. Englisch. Nº de ref. del artículo: 9783639703467
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Software Defect Prediction Using Bayesian Networks and Kernel Methods
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Software Defect Prediction Using Bayesian Networks and Kernel Methods
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Software Defect Prediction using Bayesian Networks and Kernel Methods
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Taschenbuch. Condición: Neu. Software Defect Prediction using Bayesian Networks and Kernel Methods | Ahmet Okutan | Taschenbuch | 168 S. | Englisch | 2015 | Scholars' Press | EAN 9783639703467 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Nº de ref. del artículo: 104749682
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Software Defect Prediction using Bayesian Networks and Kernel Methods
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Scholars' Press Mär 2015, 2015
ISBN 10: 3639703464
ISBN 13: 9783639703467
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. In this book, we use Bayesian modeling to determine the influential relationships among software metrics and defect proneness. Furthermore, we propose a novel technique for defect prediction that uses plagiarism detection tools. We use kernel programming to model the relationship between source code similarity and defectiveness and suggest that source code similarity is a good means of predicting both defectiveness and the number of defects in software systems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 168 pp. Englisch. Nº de ref. del artículo: 9783639703467
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Software Defect Prediction using Bayesian Networks and Kernel Methods
Impresión bajo demandaLibrería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. In this book, we use Bayesian modeling to determine the influential relationships among software metrics and defect proneness. Furthermore, we propose a novel technique for defect prediction that uses plagiarism detection tools. We use kernel programming to model the relationship between source code similarity and defectiveness and suggest that source code similarity is a good means of predicting both defectiveness and the number of defects in software systems. Nº de ref. del artículo: 9783639703467
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