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Learning Extended Finite State Machines

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Software Engineering and Formal Methods (SEFM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8702))

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Abstract

We 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.

Supported in part by the European FP7 project CONNECT (IST 231167), and by the UPMARC centre of excellence.

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Cassel, S., Howar, F., Jonsson, B., Steffen, B. (2014). Learning Extended Finite State Machines. In: Giannakopoulou, D., Salaün, G. (eds) Software Engineering and Formal Methods. SEFM 2014. Lecture Notes in Computer Science, vol 8702. Springer, Cham. https://doi.org/10.1007/978-3-319-10431-7_18

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  • DOI: https://doi.org/10.1007/978-3-319-10431-7_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10430-0

  • Online ISBN: 978-3-319-10431-7

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