2003 | OriginalPaper | Buchkapitel
Domain-Specific Optimization in Automata Learning
verfasst von : Hardi Hungar, Oliver Niese, Bernhard Steffen
Erschienen in: Computer Aided Verification
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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Automatically generated models may provide the key towards controlling the evolution of complex systems, form the basis for test generation and may be applied as monitors for running applications. However, the practicality of automata learning is currently largely preempted by its extremely high complexity and unrealistic frame conditions. By optimizing a standard learning method according to domain-specific structural properties, we are able to generate abstract models for complex reactive systems. The experiments conducted using an industry-level test environment on a recent version of a telephone switch illustrate the drastic effect of our optimizations on the learning efficiency. From a conceptual point of view, the developments can be seen as an instance of optimizing general learning procedures by capitalizing on specific application profiles.