Name: Machine Learning for Dynamic Software Analysis: Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 20 (en Inglés)
SKU: 58633227
Price: 1061.10 MXN
Availability: InStock
Author: Bennaceur, Amel ; Hähnle, Reiner ; Meinke, Karl
ISBN: 9783319965611

portada Machine Learning for Dynamic Software Analysis: Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 20 (en Inglés)

Machine Learning for Dynamic Software Analysis: Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 20 (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Bennaceur, Amel ; Hähnle, Reiner ; Meinke, Karl

Idioma

Inglés

N° páginas

257

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x1.4 cm

Peso

0.38 kg.

ISBN13

9783319965611

Categorías

Programación informática/desarrollo de software
Inteligencia artificial

Machine Learning for Dynamic Software Analysis: Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 20 (en Inglés)

Bennaceur, Amel ; Hähnle, Reiner ; Meinke, Karl (Autor) · Springer · Tapa Blanda

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Reseña del libro "Machine Learning for Dynamic Software Analysis: Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 20 (en Inglés)"

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.