Abstract
Adaptive experiments are well defined in the context of finite state machine (FSM) based analysis, in particular, in FSM based testing where homing and distinguishing experiments with FSMs are used in test derivation. In this paper, we define and propose algorithms for deriving adaptive homing and distinguishing experiments for non-initialized nondeterministic finite state machines (NFSM). For NFSMs, the construction of adaptive experiments is rather complex as the partition over produced outputs does not define a partition over the set of states but rather a collection of intersecting subsets, and thus, the refinement of such subsets is more difficult than the refinement of a partition. Given a complete non-initialized observable NFSM, we establish necessary and sufficient conditions for having adaptive homing and distinguishing experiments and evaluate the upper bound on the height of these experiments. Simple application examples demonstrating a proposed approach are provided.
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Kushik, N., El-Fakih, K., Yevtushenko, N. (2013). Adaptive Homing and Distinguishing Experiments for Nondeterministic Finite State Machines. In: Yenigün, H., Yilmaz, C., Ulrich, A. (eds) Testing Software and Systems. ICTSS 2013. Lecture Notes in Computer Science, vol 8254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41707-8_3
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