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Learning Extended Finite State MachinesWe present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.
Document ID
20150004089
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Cassel, Sofia (Uppsala Univ. Uppsala, Sweden)
Howar, Falk (SGT, Inc. Moffett Field, CA, United States)
Jonsson, Bengt (Uppsala Univ. Uppsala, Sweden)
Steffen, Bernhard (Dortmund Univ. Germany)
Date Acquired
April 2, 2015
Publication Date
September 1, 2014
Subject Category
Computer Programming And Software
Report/Patent Number
ARC-E-DAA-TN16017
Meeting Information
Meeting: SEFM 2014
Location: Grenoble
Country: France
Start Date: September 1, 2014
End Date: September 5, 2014
Sponsors: Institut National de Recherche d'Informatique et d'Automatique
Funding Number(s)
CONTRACT_GRANT: FP7-IST-231167
CONTRACT_GRANT: NNA14AA60C
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
Register Automata
Extended Finite State Machines
Active Automata Learning

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