Optimization models software reliability (36 resultados)

Paperback or Softback. Condición: New. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models. Book.

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Paperback. Condición: Brand New. 136 pages. 6.00x0.29x9.00 inches. In Stock.

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book begins with an introduction to software reliability, models and techniques. The book is an informative book covering the strategies needed to assess software failure behaviour and its quality, as well as the application of optimization tools for major managerial decisions related to the software development process. It features a broad range of topics including software reliability assessment and apportionment, optimal allocation and selection decisions and upgradations problems.It moves through a variety of problems related to the evolving field of optimization of software reliability engineering, including software release time, resource allocating, budget planning and warranty models, which are each explored in depth in dedicated chapters.This book provides a comprehensive insight into present-day practices in software reliability engineering, making it relevant to students, researchers, academics and practising consultants and engineers.…

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book begins with an introduction to software reliability, models and techniques. The book is an informative book covering the strategies needed to assess software failure behaviour and its quality, as well as the application of optimization tools for major managerial decisions related to the software development process. It features a broad range of topics including software reliability assessment and apportionment, optimal allocation and selection decisions and upgradations problems.It moves through a variety of problems related to the evolving field of optimization of software reliability engineering, including software release time, resource allocating, budget planning and warranty models, which are each explored in depth in dedicated chapters.This book provides a comprehensive insight into present-day practices in software reliability engineering, making it relevant to students, researchers, academics and practising consultants and engineers.…

Taschenbuch. Condición: Neu. Optimization Models in Software Reliability | Anu G. Aggarwal (u. a.) | Taschenbuch | xii | Englisch | 2022 | Springer | EAN 9783030789213 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condición: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

Condición: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

Paperback. Condición: Brand New. 385 pages. 9.25x6.10x0.87 inches. In Stock.

Hardcover. Condición: Brand New. 385 pages. 9.25x6.10x9.21 inches. In Stock.

Paperback. Condición: new. Paperback. Software reliability is an important aspect of software engineering because the ability of a software system to operate without failure influences quality, maintainability, availability, and user confidence. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models provides a focused technical examination of software reliability prediction, machine learning, optimization, reliability modeling, and the application of the Jaya optimization algorithm to software engineering problems.The book introduces the fundamental concepts of software reliability and explains the importance of predicting software failures during the development and testing lifecycle. Reliability prediction can help software engineers understand failure behavior, estimate future reliability, identify potential weaknesses, and support decisions related to testing and quality assurance. Traditional software reliability models provide useful mathematical frameworks, but their effectiveness may depend on assumptions about failure processes and available project data.A central focus is placed on machine learning-based software reliability prediction. Machine learning methods can identify patterns within historical software failure and testing data and use these patterns to estimate future reliability behavior. The book discusses general concepts associated with data preparation, feature selection, model development, training, prediction, validation, and performance evaluation.The Jaya optimization algorithm is examined as an optimization technique for improving predictive modeling. Optimization can be used to identify suitable parameter combinations, select relevant variables, or improve the performance of machine learning models. The book explores the principles of Jaya optimization and its potential integration with machine learning approaches for software reliability prediction.The hybrid methodology forms an important part of the book. Combining optimization with machine learning can provide a systematic framework for addressing challenges associated with model parameters, feature selection, and predictive performance. The book considers how an optimization layer can support the development of more effective reliability prediction models while recognizing the importance of appropriate datasets, model validation, and evaluation criteria.Software reliability data and failure behavior are also examined from a predictive modeling perspective. Software testing can generate observations related to failures, execution time, fault occurrence, and other reliability indicators. Preparing such data appropriately is essential for developing predictive models capable of capturing meaningful relationships between input variables and reliability outcomes. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models examines machine learning and optimization techniques for predicting software reliability and failure behavior This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 136 pp. Englisch.

Paperback. Condición: new. Paperback. Software reliability is an important aspect of software engineering because the ability of a software system to operate without failure influences quality, maintainability, availability, and user confidence. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models provides a focused technical examination of software reliability prediction, machine learning, optimization, reliability modeling, and the application of the Jaya optimization algorithm to software engineering problems.The book introduces the fundamental concepts of software reliability and explains the importance of predicting software failures during the development and testing lifecycle. Reliability prediction can help software engineers understand failure behavior, estimate future reliability, identify potential weaknesses, and support decisions related to testing and quality assurance. Traditional software reliability models provide useful mathematical frameworks, but their effectiveness may depend on assumptions about failure processes and available project data.A central focus is placed on machine learning-based software reliability prediction. Machine learning methods can identify patterns within historical software failure and testing data and use these patterns to estimate future reliability behavior. The book discusses general concepts associated with data preparation, feature selection, model development, training, prediction, validation, and performance evaluation.The Jaya optimization algorithm is examined as an optimization technique for improving predictive modeling. Optimization can be used to identify suitable parameter combinations, select relevant variables, or improve the performance of machine learning models. The book explores the principles of Jaya optimization and its potential integration with machine learning approaches for software reliability prediction.The hybrid methodology forms an important part of the book. Combining optimization with machine learning can provide a systematic framework for addressing challenges associated with model parameters, feature selection, and predictive performance. The book considers how an optimization layer can support the development of more effective reliability prediction models while recognizing the importance of appropriate datasets, model validation, and evaluation criteria.Software reliability data and failure behavior are also examined from a predictive modeling perspective. Software testing can generate observations related to failures, execution time, fault occurrence, and other reliability indicators. Preparing such data appropriately is essential for developing predictive models capable of capturing meaningful relationships between input variables and reliability outcomes. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models examines machine learning and optimization techniques for predicting software reliability and failure behavior This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Software reliability is an important aspect of software engineering because the ability of a software system to operate without failure influences quality, maintainability, availability, and user confidence. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models provides a focused technical examination of software reliability prediction, machine learning, optimization, reliability modeling, and the application of the Jaya optimization algorithm to software engineering problems.The book introduces the fundamental concepts of software reliability and explains the importance of predicting software failures during the development and testing lifecycle. Reliability prediction can help software engineers understand failure behavior, estimate future reliability, identify potential weaknesses, and support decisions related to testing and quality assurance. Traditional software reliability models provide useful mathematical frameworks, but their effectiveness may depend on assumptions about failure processes and available project data.A central focus is placed on machine learning-based software reliability prediction. Machine learning methods can identify patterns within historical software failure and testing data and use these patterns to estimate future reliability behavior. The book discusses general concepts associated with data preparation, feature selection, model development, training, prediction, validation, and performance evaluation.The Jaya optimization algorithm is examined as an optimization technique for improving predictive modeling. Optimization can be used to identify suitable parameter combinations, select relevant variables, or improve the performance of machine learning models. The book explores the principles of Jaya optimization and its potential integration with machine learning approaches for software reliability prediction.The hybrid methodology forms an important part of the book. Combining optimization with machine learning can provide a systematic framework for addressing challenges associated with model parameters, feature selection, and predictive performance. The book considers how an optimization layer can support the development of more effective reliability prediction models while recognizing the importance of appropriate datasets, model validation, and evaluation criteria.Software reliability data and failure behavior are also examined from a predictive modeling perspective. Software testing can generate observations related to failures, execution time, fault occurrence, and other reliability indicators. Preparing such data appropriately is essential for developing predictive models capable of capturing meaningful relationships between input variables and reliability outcomes. …

Paperback. Condición: new. Paperback. Software reliability is an important aspect of software engineering because the ability of a software system to operate without failure influences quality, maintainability, availability, and user confidence. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models provides a focused technical examination of software reliability prediction, machine learning, optimization, reliability modeling, and the application of the Jaya optimization algorithm to software engineering problems.The book introduces the fundamental concepts of software reliability and explains the importance of predicting software failures during the development and testing lifecycle. Reliability prediction can help software engineers understand failure behavior, estimate future reliability, identify potential weaknesses, and support decisions related to testing and quality assurance. Traditional software reliability models provide useful mathematical frameworks, but their effectiveness may depend on assumptions about failure processes and available project data.A central focus is placed on machine learning-based software reliability prediction. Machine learning methods can identify patterns within historical software failure and testing data and use these patterns to estimate future reliability behavior. The book discusses general concepts associated with data preparation, feature selection, model development, training, prediction, validation, and performance evaluation.The Jaya optimization algorithm is examined as an optimization technique for improving predictive modeling. Optimization can be used to identify suitable parameter combinations, select relevant variables, or improve the performance of machine learning models. The book explores the principles of Jaya optimization and its potential integration with machine learning approaches for software reliability prediction.The hybrid methodology forms an important part of the book. Combining optimization with machine learning can provide a systematic framework for addressing challenges associated with model parameters, feature selection, and predictive performance. The book considers how an optimization layer can support the development of more effective reliability prediction models while recognizing the importance of appropriate datasets, model validation, and evaluation criteria.Software reliability data and failure behavior are also examined from a predictive modeling perspective. Software testing can generate observations related to failures, execution time, fault occurrence, and other reliability indicators. Preparing such data appropriately is essential for developing predictive models capable of capturing meaningful relationships between input variables and reliability outcomes. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models examines machine learning and optimization techniques for predicting software reliability and failure behavior This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models | Solanki | Taschenbuch | Englisch | 2026 | SHARK NAIL | EAN 9781962116626 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Condición: new. Questo è un articolo print on demand.

Condición: new. Questo è un articolo print on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book begins with an introduction to software reliability, models and techniques. The book is an informative book covering the strategies needed to assess software failure behaviour and its quality, as well as the application of optimization tools for major managerial decisions related to the software development process. It features a broad range of topics including software reliability assessment and apportionment, optimal allocation and selection decisions and upgradations problems.It moves through a variety of problems related to the evolving field of optimization of software reliability engineering, including software release time, resource allocating, budget planning and warranty models, which are each explored in depth in dedicated chapters.This book provides a comprehensive insight into present-day practices in software reliability engineering, making it relevant to students, researchers, academics and practising consultants and engineers. 388 pp. Englisch. …

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book begins with an introduction to software reliability, models and techniques. The book is an informative book covering the strategies needed to assess software failure behaviour and its quality, as well as the application of optimization tools for.…

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book begins with an introduction to software reliability, models and techniques. The book is an informative book covering the strategies needed to assess software failure behaviour and its quality, as well as the application of optimization tools for.…

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book begins with an introduction to software reliability, models and techniques. The book is an informative book covering the strategies needed to assess software failure behaviour and its quality, as well as the application of optimization tools for major managerial decisions related to the software development process. It features a broad range of topics including software reliability assessment and apportionment, optimal allocation and selection decisions and upgradations problems.It moves through a variety of problems related to the evolving field of optimization of software reliability engineering, including software release time, resource allocating, budget planning and warranty models, which are each explored in depth in dedicated chapters.This book provides a comprehensive insight into present-day practices in software reliability engineering, making it relevant to students, researchers, academics and practising consultants and engineers. 388 pp. Englisch.…