LearnLib Tutorial: From Finite Automata to Register Interface Programs

In the past decade, active automata learning, an originally merely theoretical enterprise, got attention as a method for dealing with black-box or third party systems. Applications ranged from the support of formal verification, e.g. for assume guarantee reasoning [4], to usage of learned models as the basis for regression testing. In the meantime, a number of approaches exploiting active learning for validation [17,20,6,7,2,1] emerged. Today, active automata learning is on the verge of becoming a valuable asset in bringing formal methods to systems lacking formal descriptions (e.g., the huge class of legacy systems): This edition of ISoLA alone features a track on active learning in formal verification [16], one on model-based testing and model inference [12], this tutorial, and is co-located with the STRESS summer school,3 where active automata learning is part of the curriculum. In particular when dealing with black-box systems, i.e., systems that can be observed, but for which no or little knowledge about the internal structure or even their intent is available, active automata learning can be considered as a key technology due to its test-based approach to model inference. However, the testbased interaction introduces a number of challenges when using active automata learning to infer models of real word systems, which have been summarized in [21]: