Machine Learning for Dynamic Software Analysis: Potentials and Limits

✍ Scribed by Amel Bennaceur, Reiner Hähnle, Karl Meinke

Publisher: Springer International Publishing
Year: 2018
Tongue: English
Leaves: 260
Series: Lecture Notes in Computer Science 11026
Edition: 1st ed.
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages I-IX
Front Matter ....Pages 1-1
Machine Learning for Software Analysis: Models, Methods, and Applications (Amel Bennaceur, Karl Meinke)....Pages 3-49
Front Matter ....Pages 51-51
Learning-Based Testing: Recent Progress and Future Prospects (Karl Meinke)....Pages 53-73
Model Learning and Model-Based Testing (Bernhard K. Aichernig, Wojciech Mostowski, Mohammad Reza Mousavi, Martin Tappler, Masoumeh Taromirad)....Pages 74-100
Testing Functional Black-Box Programs Without a Specification (Neil Walkinshaw)....Pages 101-120
Front Matter ....Pages 121-121
Active Automata Learning in Practice (Falk Howar, Bernhard Steffen)....Pages 123-148
Extending Automata Learning to Extended Finite State Machines (Sofia Cassel, Falk Howar, Bengt Jonsson, Bernhard Steffen)....Pages 149-177
Inferring FSM Models of Systems Without Reset (Roland Groz, Adenilso Simao, Alexandre Petrenko, Catherine Oriat)....Pages 178-201
Front Matter ....Pages 203-203
Constraint-Based Behavioral Consistency of Evolving Software Systems (Reiner Hähnle, Bernhard Steffen)....Pages 205-218
Logic-Based Learning: Theory and Application (Dalal Alrajeh, Alessandra Russo)....Pages 219-256
Back Matter ....Pages 257-257

✦ Subjects

Computer Science; Software Engineering/Programming and Operating Systems; Artificial Intelligence (incl. Robotics); Theory of Computation

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