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ORCIDOutcome of a Dagstuhl Seminar: Machine Learning for Dynamic Software Analysis 2016
- Amel Bennaceur, Reiner Hähnle, Karl Meinke:
Machine Learning for Dynamic Software Analysis: Potentials and Limits - International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers. Lecture Notes in Computer Science 11026, Springer 2018, ISBN 978-3-319-96561-1
Introduction
- Amel Bennaceur, Karl Meinke:
Machine Learning for Software Analysis: Models, Methods, and Applications. 3-49
Testing and Learning
- Karl Meinke:
Learning-Based Testing: Recent Progress and Future Prospects. 53-73 - Bernhard K. Aichernig
, Wojciech Mostowski, Mohammad Reza Mousavi, Martin Tappler, Masoumeh Taromirad:
Model Learning and Model-Based Testing. 74-100 - Neil Walkinshaw:
Testing Functional Black-Box Programs Without a Specification. 101-120
Extensions of Automata Learning
- Falk Howar, Bernhard Steffen:
Active Automata Learning in Practice - An Annotated Bibliography of the Years 2011 to 2016. 123-148 - Sofia Cassel, Falk Howar, Bengt Jonsson, Bernhard Steffen:
Extending Automata Learning to Extended Finite State Machines. 149-177 - Roland Groz, Adenilso da Silva Simão, Alexandre Petrenko, Catherine Oriat:
Inferring FSM Models of Systems Without Reset. 178-201
Integrative Approaches
- Reiner Hähnle, Bernhard Steffen:
Constraint-Based Behavioral Consistency of Evolving Software Systems. 205-218 - Dalal Alrajeh, Alessandra Russo:
Logic-Based Learning: Theory and Application. 219-256
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