Conformance testing for finite state machines and regular inference both aim at identifying the model structure underlying a
black box system
on the basis of a limited set of observations. Whereas the former technique
for equivalence with a
conjecture model, the latter techniques addresses the corresponding
problem by means of techniques adopted from automata learning. In this paper we establish a common framework to investigate the similarities of these techniques by showing how results in one area can be transferred to results in the other and to explain the reasons for their differences.
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