Learning Register Automata with Fresh Value Generation

We present a new algorithm for active learning of register automata. Our algorithm uses counterexample-guided abstraction refinement to automatically construct a component which maps in a history dependent manner the large set of actions of an implementation into a small set of actions that can be handled by a Mealy machine learner. The class of register automata that is handled by our algorithm extends previous definitions since it allows for the generation of fresh output values. This feature is crucial in many real-world systems e.g. servers that generate identifiers, passwords or sequence numbers. We have implemented our new algorithm in a tool called Tomte.

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