Abstract
In this tool demonstration 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.
The demonstration of the LearnLib will include the extrapolation of a behavioral model for a realistic (legacy) system, and the statistical analysis of different variants of automata learning algorithms on the basis of random generated models.
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Raffelt, H., Steffen, B. (2006). LearnLib: A Library for Automata Learning and Experimentation. In: Baresi, L., Heckel, R. (eds) Fundamental Approaches to Software Engineering. FASE 2006. Lecture Notes in Computer Science, vol 3922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11693017_28
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DOI: https://doi.org/10.1007/11693017_28
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