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Extending Automata Learning to Extended Finite State Machines
Scania CV AB, Sodertalje, Sweden.
Dortmund Univ Technol, Dortmund, Germany;Fraunhofer ISST, Dortmund, Germany.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
TU Dortmund, Chair Programming Syst, Dortmund, Germany.
2018 (English)In: Machine Learning for Dynamic Software Analysis: Potentials and Limits / [ed] Bennaceur, A Hahnle, R Meinke, K, Springer, 2018, p. 149-177Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2018. p. 149-177
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11026
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-391442DOI: 10.1007/978-3-319-96562-8_6ISI: 000476941200006ISBN: 978-3-319-96562-8 (electronic)ISBN: 978-3-319-96561-1 (print)OAI: oai:DiVA.org:uu-391442DiVA, id: diva2:1345042
Conference
International Dagstuhl Seminar on 16172 - Machine Learning for Dynamic Software Analysis - Potentials and Limits, APR 24-27, 2016, Wadern, GERMANY
Funder
EU, FP7, Seventh Framework Programme, IST 231167Swedish Research CouncilAvailable from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved

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Jonsson, Bengt

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CiteExportLink to record
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