Abstract
Inferring a minimal finite state machine (FSM) from a given set of traces is a fundamental problem in computer science. Although the problem is known to be NP-complete, it can be solved efficiently with SAT solvers when the given set of traces is relatively small. On the other hand, to infer an FSM equivalent to a machine which generates traces, the set of traces should be sufficiently representative and hence large. However, the existing SAT-based inference techniques do not scale well when the length and number of traces increase. In this paper, we propose a novel approach which processes lengthy traces incrementally. The experimental results indicate that it scales sufficiently well and time it takes grows slowly with the size of traces.
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Acknowledgements
This work was partially supported by MESI (Ministère de l’Économie, Science et Innovation) of Gouvernement du Québec, NSERC of Canada and CAE.
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Avellaneda, F., Petrenko, A. (2018). FSM Inference from Long Traces. In: Havelund, K., Peleska, J., Roscoe, B., de Vink, E. (eds) Formal Methods. FM 2018. Lecture Notes in Computer Science(), vol 10951. Springer, Cham. https://doi.org/10.1007/978-3-319-95582-7_6
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DOI: https://doi.org/10.1007/978-3-319-95582-7_6
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