machine learning dynamic software (16 résultats)

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. In.

Paperback. Etat : new. Paperback. Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches. Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : Brand New. revised edition. 268 pages. 9.25x6.10x0.61 inches. In Stock.

Taschenbuch. Etat : Neu. Neuware -Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled 'Machine Learning for Dynamic Software Analysis: Potentials and Limits¿ held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 268 pp. Englisch.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled 'Machine Learning for Dynamic Software Analysis: Potentials and Limits' held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches.

Paperback. Etat : new. Paperback. Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches. Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled 'Machine Learning for Dynamic Software Analysis: Potentials and Limits' held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches. 268 pp. Englisch.

Etat : New. Print on Demand.

Etat : New. PRINT ON DEMAND.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Written by international experts Presents the state of the art and suggests new directions and collaborations for future researchGives an overview of the machine learning techniques that can be used for software analysis &nbsp.