Machine Learning for Dynamic Software Analysis: Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, ... 11026 (Programming and Software Engineering) - Tapa blanda

 
9783319965611: Machine Learning for Dynamic Software Analysis: Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, ... 11026 (Programming and Software Engineering)
Tapa blanda
ISBN 10: 3319965611 ISBN 13: 9783319965611
Editorial: Springer, 2018

Sinopsis

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.


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Reseña del editor

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.


"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2018
Idioma
Inglés
ISBN 10 
3319965611
ISBN 13 
9783319965611
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
268
Editor
Bennaceur Amel, Hähnle Reiner, Meinke Karl
Contacto del fabricante
ProductSafety@springernature.com
ProductSafety@springernature.com

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