Inferring Finite State Machines Without Reset Using State Identification Sequences

Abstract : Identifying the (finite state) control structure of a black box system from the traces observed in finite interaction is of great interest for many model-based activities, such as model-based testing or model-driven engineering. There are several inference methods, but all those methods assume that the system can be reset whenever necessary. In this paper, we address the issue of inferring a finite state machine (FSM) that cannot be reset; we propose a method, inspired by FSM-based testing generation methods. We assume classical testing hypotheses, namely that we are given a bound n on the number of states and a set W of characterizing sequences to distinguish states. To the best of our knowledge, this is the first model inference method that does not require resetting the system, and does not require an external oracle to decide on equivalence. The length of the test sequence is polynomial in n and the exponent depends on the cardinal |W| of the characterization set.
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Roland Groz, Adenilso Simao, Alexandre Petrenko, Catherine Oriat. Inferring Finite State Machines Without Reset Using State Identification Sequences. 27th IFIP International Conference on Testing Software and Systems (ICTSS), Nov 2015, Sharjah and Dubai, United Arab Emirates. pp.161-177, ⟨10.1007/978-3-319-25945-1_10⟩. ⟨hal-01470168⟩

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