TY - JOUR
T1 - AALpy: an active automata learning library
AU - Muskardin, Edi
AU - Aichernig, Bernhard
AU - Pill, Ingo
AU - Pferscher, Andrea
AU - Tappler, Martin
PY - 2022
Y1 - 2022
N2 - AALpy is an extensible open-source Python library providing efficient implementations of active automata learning algorithms for deterministic, non-deterministic, and stochastic systems. We put a special focus on the conformance testing aspect in active automata learning, as well as on an intuitive and seamlessly integrated interface for learning automata characterizing real-world reactive systems. In this article, we present AALpy’s core functionalities, illustrate its usage via examples, and evaluate its learning performance. Finally, we present selected case studies on learning models of various types of systems with AALpy.
AB - AALpy is an extensible open-source Python library providing efficient implementations of active automata learning algorithms for deterministic, non-deterministic, and stochastic systems. We put a special focus on the conformance testing aspect in active automata learning, as well as on an intuitive and seamlessly integrated interface for learning automata characterizing real-world reactive systems. In this article, we present AALpy’s core functionalities, illustrate its usage via examples, and evaluate its learning performance. Finally, we present selected case studies on learning models of various types of systems with AALpy.
U2 - 10.1007/S11334-022-00449-3
DO - 10.1007/S11334-022-00449-3
M3 - Artikel
SN - 1614-5046
VL - 18
SP - 417
EP - 426
JO - Innovations in Systems and Software Engineering
JF - Innovations in Systems and Software Engineering
ER -