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Extending automata learning to extended finite state machines

: Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard


Bennaceur, A.:
Machine learning for dynamic software analysis: Potentials and limits : International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016; Revised papers
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 11026)
ISBN: 978-3-319-96561-1 (Print)
ISBN: 978-3-319-96562-8 (Online)
Dagstuhl Seminar "Machine Learning for Dynamic Software Analysis" <2016, Dagstuhl>
Fraunhofer ISST ()

Automata learning is an established class of techniques for inferring automata models by observing how they respond to a sample of input words. Recently, approaches have been presented that extend these techniques to infer extended finite state machines (EFSMs) by dynamic black-box analysis. EFSMs model both data flow and control behavior, and their mutual interaction. Different dialects of EFSMs are widely used in tools for model-based software development, verification, and testing. This survey paper presents general principles behind some of these recent extensions. The goal is to elucidate how the principles behind classic automata learning can be maintained and guide extensions to more general automata models, and to situate some extensions with respect to these principles.