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 …

In this paper we present TTT, a novel active automata learning algorithm formulated in the
Minimally Adequate Teacher (MAT) framework. The distinguishing characteristic of TTT is its …

We present L#, a new and simple approach to active automata learning. Instead of focusing
on equivalence of observations, like the L∗ algorithm and its descendants, L# takes a …

We present a black-box active learning algorithm for inferring extended finite state machines
(EFSM) s by dynamic black-box analysis. EFSMs can be used to model both data flow and …

We describe a large collection of benchmarks, publicly available through the wiki automata.
cs. ru. nl, of different types of state machine models: DFAs, Moore machines, Mealy …

Software systems are present all around us and playing their vital roles in our daily life. The
correct functioning of these systems is of prime concern. In addition to classical testing …

The wealth of model-based techniques in software engineering—such as model checking or
model-based testing—is starkly contrasted with a frequent lack of formal models in practical …

We present a new algorithm for active learning of register automata. Our algorithm uses
counterexample-guided abstraction refinement to automatically construct a component …

Accurate description of program inputs remains a critical challenge in the field of
programming languages. Active learning, as a well-established field, achieves exact …

This paper presents a compositional approach to active automata learning of Systems of
Procedural Automata (SPAs), an extension of Deterministic Finite Automata (DFAs) to …