AALpy: an active automata learning library

Autor: Edi Muškardin, Bernhard K. Aichernig, Ingo Pill, Andrea Pferscher, Martin Tappler
Rok vydání: 2022
Předmět:
Zdroj: Innovations in Systems and Software Engineering. 18:417-426
ISSN: 1614-5054
1614-5046
Popis: 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.
Databáze: OpenAIRE
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