Technical report demonstrates a modular automata learning framework, highlighting order-of-magnitude efficiency gains achieved by configuring domain-specific optimizations for realistic systems.
In this paper we present the LearnLib, a library for automata learning and experimentation. Its modular structure allows users to configure their tailored learning scenarios, which exploit specific properties of the envisioned applications. As has been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance gains of several orders of magnitude, a necessary precondition to make automata learning applicable to realistic scenarios.
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Raffelt et al. (2005) studied this question.
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