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In this paper, we present LearnLib, a library for active automata learning. The current, open-source version of LearnLib was completely rewritten from scratch, incorporating the lessons learned from the decade-spanning development process of the previous versions of LearnLib. Like its immediate predecessor, the open-source LearnLib is written in Java to enable a high degree of flexibility and extensibility, while at the same time providing a performance that allows for large-scale applications. Additionally, LearnLib provides facilities for visualizing the progress of learning algorithms in detail, thus complementing its applicability in research and industrial contexts with an educational aspect.

This paper reviews the development of Register Automaton learning, an enhancement of active automata learning to deal with infinite-state systems. We will revisit the precursor techniques and influences, which in total span over more than a decade. A large share of this development was guided and motivated by the increasingly popular application of grammatical inference techniques in the field of software engineering. We specifically focus on a key problem to achieve practicality in this field: the adequate treatment of data values ranging over infinite domains, a major source of undecidability. Starting with the first case studies, in which data was completely abstracted away, we revisit different steps towards dealing with data explicitly at a model level: we discuss Mealy machines as a model for systems with (data) output, automated alphabet abstraction refinement techniques as a twodimensional extension of the partition-refinement based approach of active automata learning to also inferring optimal alphabet abstractions, and Register Mealy Machines, which can be regarded as programs restricted to data-independent data processing as it is typical for protocols or interface programs. We are convinced that this development will significantly contribute to paving the road for active automata learning to become a technology of high practical importance.

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