AALpy: an active automata learning library
Autor: | Edi Muškardin, Bernhard K. Aichernig, Ingo Pill, Andrea Pferscher, Martin Tappler |
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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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