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

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11026)

Also part of the Programming and Software Engineering book sub series (LNPSE, volume 11026)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Introduction

    1. Front Matter
      Pages 1-1
  3. Testing and Learning

    1. Front Matter
      Pages 51-51
    2. Bernhard K. Aichernig, Wojciech Mostowski, Mohammad Reza Mousavi, Martin Tappler, Masoumeh Taromirad
      Pages 74-100
  4. Extensions of Automata Learning

    1. Front Matter
      Pages 121-121
    2. Falk Howar, Bernhard Steffen
      Pages 123-148
    3. Sofia Cassel, Falk Howar, Bengt Jonsson, Bernhard Steffen
      Pages 149-177
    4. Roland Groz, Adenilso Simao, Alexandre Petrenko, Catherine Oriat
      Pages 178-201
  5. Integrative Approaches

    1. Front Matter
      Pages 203-203
    2. Reiner Hähnle, Bernhard Steffen
      Pages 205-218
    3. Dalal Alrajeh, Alessandra Russo
      Pages 219-256
  6. Back Matter
    Pages 257-257

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.


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

  1. 1.The Open UniversityMilton KeynesUnited Kingdom
  2. 2.Technische Universität DarmstadtDarmstadtGermany
  3. 3.KTH Royal Institute of TechnologyStockholmSweden

Bibliographic information