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Книги на английски > Computing & Information Technology > Computer science > Artificial intelligence > Machine learning > Machine Learning for Dynamic Software Analysis: Potentials and Limits

Machine Learning for Dynamic Software Analysis: Potentials and Limits

Name: Machine Learning for Dynamic Software Analysis: Potentials and Limits
Brand: Springer International Publishing AG
SKU: 19728419
Price: 59.95 EUR
Availability: InStock
Author: Amel Bennaceur
ISBN: 9783319965611

International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers

Amel Bennaceur

Reiner Hähnle

Karl Meinke

Език

Английски език

Книга С меки корици

Код Либристо: 19728419

Издателство Springer International Publishing AG, юли 2018

Machine learning of software artefacts is an emerging area of interaction between the machine learni... Цялото описание

Код Либристо: 19728419

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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.