Editors:
- Written by international experts
- Presents the state of the art and suggests new directions and collaborations for future research
- Gives an overview of the machine learning techniques that can be used for software analysis
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11026)
Part of the book sub series: Programming and Software Engineering (LNPSE)
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Table of contents (9 chapters)
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Front Matter
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Introduction
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Front Matter
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Extensions of Automata Learning
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Front Matter
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Integrative Approaches
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Front Matter
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Back Matter
About this book
Keywords
- Active learning
- Artificial intelligence
- Automated static analysis
- Computing methodologies
- Dynamic analysis
- Formal languages and automata theory
- Formal methods
- Machine learning
- Model development and analysis
- Semantics
- Software design
- Software engineering
- Specifications
- Theory and algorithms for application domains
- Theory of computation
Editors and Affiliations
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The Open University, Milton Keynes, United Kingdom
Amel Bennaceur
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Technische Universität Darmstadt, Darmstadt, Germany
Reiner Hähnle
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KTH Royal Institute of Technology, Stockholm, Sweden
Karl Meinke
Bibliographic Information
Book Title: Machine Learning for Dynamic Software Analysis: Potentials and Limits
Book Subtitle: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers
Editors: Amel Bennaceur, Reiner Hähnle, Karl Meinke
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-319-96562-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Softcover ISBN: 978-3-319-96561-1Published: 21 July 2018
eBook ISBN: 978-3-319-96562-8Published: 20 July 2018
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: IX, 257
Number of Illustrations: 38 b/w illustrations
Topics: Software Engineering/Programming and Operating Systems, Artificial Intelligence, Theory of Computation