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Machine Learning for Dynamic Software Analysis: Potentials and Limits

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

  • Book
  • © 2018

Overview

Editors:
  1. Amel Bennaceur

    1. The Open University, Milton Keynes, United Kingdom

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  2. Reiner Hähnle

    1. Technische Universität Darmstadt, Darmstadt, Germany

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  3. Karl Meinke

    1. KTH Royal Institute of Technology, Stockholm, Sweden

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  • 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)

  1. Front Matter

    Pages I-IX
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Machine Learning for Software Analysis: Models, Methods, and Applications

      • Amel Bennaceur, Karl Meinke
      Pages 3-49

  3. Testing and Learning

    1. Front Matter

      Pages 51-51
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    2. Learning-Based Testing: Recent Progress and Future Prospects

      • Karl Meinke
      Pages 53-73

    3. Model Learning and Model-Based Testing

      • Bernhard K. Aichernig, Wojciech Mostowski, Mohammad Reza Mousavi, Martin Tappler, Masoumeh Taromirad
      Pages 74-100

    4. Testing Functional Black-Box Programs Without a Specification

      • Neil Walkinshaw
      Pages 101-120

  4. Extensions of Automata Learning

    1. Front Matter

      Pages 121-121
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    2. Active Automata Learning in Practice

      • Falk Howar, Bernhard Steffen
      Pages 123-148

    3. Extending Automata Learning to Extended Finite State Machines

      • Sofia Cassel, Falk Howar, Bengt Jonsson, Bernhard Steffen
      Pages 149-177

    4. Inferring FSM Models of Systems Without Reset

      • Roland Groz, Adenilso Simao, Alexandre Petrenko, Catherine Oriat
      Pages 178-201

  5. Integrative Approaches

    1. Front Matter

      Pages 203-203
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    2. Constraint-Based Behavioral Consistency of Evolving Software Systems

      • Reiner Hähnle, Bernhard Steffen
      Pages 205-218

    3. Logic-Based Learning: Theory and Application

      • Dalal Alrajeh, Alessandra Russo
      Pages 219-256

  6. Back Matter

    Pages 257-257
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Keywords

About this book

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.


Editors and Affiliations

  • The Open University, Milton Keynes, United Kingdom

    Amel Bennaceur

  • Technische Universität Darmstadt, Darmstadt, Germany

    Reiner Hähnle

  • 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

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