DFAMiner: An efficient tool for learning minimal separating DFAs from labelled samples

We introduce DFAMiner , an efficient tool for learning minimal separating deterministic finite automata (DFA) from a set of labelled samples. The significant improvement of DFAMiner over existing tools is the use of an intermediate representation called three-valued automaton for the given set of labelled samples. This three-valued automaton has accepting and rejecting states as well as don’t-care states, so that it can exactly recognise the labelled samples. The minimal separating DFA for the labelled samples is then learned by minimising the constructed three-valued automata via a reduction to SAT solving. Separating automata are an interesting class of automata that occurs generally in regular model checking and has raised interest in foundational questions of parity game solving. Therefore, DFAMiner has the potential to further advance these fields.