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 manuscript, we present AALpy’s core functionalities, illustrate its usage via examples, and evaluate its learning performance.
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Code, documentation, interactive examples, and a comprehensive Wiki can be found at https://github.com/DES-Lab/AALpy.
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Acknowledgments
This work has been supported by the “University SAL Labs” initiative of Silicon Austria Labs (SAL) and its Austrian partner universities for applied fundamental research for electronic based systems and by the TU Graz LEAD project “Dependable Internet of Things in Adverse Environments”.
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Muškardin, E., Aichernig, B.K., Pill, I., Pferscher, A., Tappler, M. (2021). AALpy: An Active Automata Learning Library. In: Hou, Z., Ganesh, V. (eds) Automated Technology for Verification and Analysis. ATVA 2021. Lecture Notes in Computer Science(), vol 12971. Springer, Cham. https://doi.org/10.1007/978-3-030-88885-5_5
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