Inferring Non-resettable Mealy Machines with $n$ States
; Proceedings of The 13th International Conference on Grammatical Inference, PMLR 57:30-41, 2017.
Automata inference algorithms are used to extract behavioural models of software components. However, when the software system cannot be reset, inference must be done from a single trace. This paper proposes an active learning algorithm that can infer a Mealy model under the assumption that the number of the states of the machine is known and that a characterization set for it is provided. This algorithm improves on a previous paper that used a looser assumption on the number of states. The complexity is polynomial in the number of states of the Mealy machine.