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 …

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 …

Active automata learning is slowly becoming a standard tool in the toolbox of the software
engineer. As systems become ever more complex and development becomes more …

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 …

Abstract Model learning is a black-box technique for constructing state machine models of
software and hardware components, which has been successfully used in areas such as …

In order to facilitate model-based verification and validation, effort is underway to develop
techniques for generating models of communication system components from observations …