A Theory of History Dependent Abstractions for Learning Interface AutomataF. Aarts, F. Heidarian, and F.W. Vaandrager. A Theory of History Dependent Abstractions for Learning Interface Automata. To appear in Proceedings 23rd International Conference on Concurrency Theory (CONCUR), Newcastle upon Tyne, UK, September 3-8, 2012.
AbstractHistory dependent abstraction operators are the key for scaling existing methods for active learning of automata to realistic applications. Recently, Aarts, Jonsson & Uijen have proposed a framework for history dependent abstraction operators. Using this framework they succeeded to automatically infer models of several realistic software components with large state spaces, including fragments of the TCP and SIP protocols. Despite this success, the approach of Aarts et al suffers from limitations that seriously hinder its applicability in practice. In this article, we get rid of some of these limitations and present four important generalizations/improvements of the theory of history dependent abstraction operators. Our abstraction framework supports: (a) interface automata instead of the more restricted Mealy machines, (b) the concept of a learning purpose, which allows one to restrict the learning process to relevant behaviors only, (c) a richer class of abstractions, which includes abstractions that overapproximate the behavior of the system-under-test, and (d) a conceptually superior approach for testing correctness of the hypotheses that are generated by the learner.
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