AALpy: an active automata learning library
Edi Muškardin, Bernhard K. Aichernig, Ingo Pill, Andrea Pferscher, Martin Tappler
Abstract
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 .
Topics & Concepts
Python (programming language)Computer scienceLearning automataAutomatonActive learning (machine learning)ImplementationExtensibilityProgramming languageTheoretical computer scienceArtificial intelligenceMachine Learning and AlgorithmsSoftware Testing and Debugging TechniquesFormal Methods in Verification