Inferring Symbolic Automata
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We study the learnability of symbolic finite state automata, a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the query learning paradigm, and so far all obtained results are positive. We provide a necessary condition for efficient learnability of SFAs in this paradigm, from which we obtain the first negative result. Most of this work studies learnability of SFAs under the paradigm of identification in the limit using polynomial time and data. We provide a sufficient condition for efficient learnability of SFAs in this paradigm, as well as a necessary condition, and provide several positive and negative results.
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