AALpy: an active automata learning library (Extended Version)

Edi Muskardin, Bernhard K. Aichernig, Ingo Pill, Andrea Pferscher, Martin Tappler

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
JournalInnovations in Systems and Software Engineering
Publication statusPublished - 2022

Keywords

  • Active Automata Learning
  • Model Inferace
  • Testing
  • Python

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