AALpy: An active automata learning library

Edi Muškardin*, Bernhard Aichernig, Ingo Pill, Andrea Pferscher, Martin Tappler

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung

Abstract

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.

Originalspracheenglisch
Seiten (von - bis)417-426
Seitenumfang10
FachzeitschriftInnovations in Systems and Software Engineering
Jahrgang18
Ausgabenummer3
DOIs
PublikationsstatusVeröffentlicht - Sept. 2022

ASJC Scopus subject areas

  • Theoretische Informatik
  • Informatik (insg.)

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