Parametric random generation of deterministic tree automata

AbstractUniform random generators deliver a simple empirical means to estimate the average complexity of an algorithm. We present a general rejection algorithm that generates sequential letter-to-letter transducers up to isomorphism. We also propose an original parametric random generation algorithm to produce sequential letter-to-letter transducers with a fixed number of transitions. We tailor this general scheme to randomly generate deterministic tree walking automata and deterministic top–down tree automata. We apply our implementation of the generator to the estimation of the average complexity of a deterministic tree walking automata to nondeterministic top–down tree automata construction we also implemented