Machine learning for dynamic software analysis : potentials and limits : International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised papers / Amel Bennaceur, Reiner Hähnle, Karl Meinke (eds.).

Contributor(s):

Material type: Text

TextSeries: Lecture notes in computer science ; 11026. | LNCS sublibrary. SL 2, Programming and software engineering.Publisher: Cham, Switzerland : Springer, 2018Description: 1 online resource (ix, 257 pages) : illustrationsContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319965628
331996562X

Subject(s):

Genre/Form:

Additional physical formats: Printed edition:: No titleDDC classification:

006.3/1 23

LOC classification:

Q325.5

Online resources:

Click here to access online

Contents:

Introduction -- Testing and Learning -- Extensions of Automata Learning -- Integrative Approaches.

Summary: 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

Holdings ( 1 )
Title notes ( 4 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
eBook	e-Library	eBook LNCS		Available

Total holds: 0

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed July 26, 2018).

Introduction -- Testing and Learning -- Extensions of Automata Learning -- Integrative Approaches.

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