AALpy: An active automata learning library

Edi Muškardin*, Bernhard Aichernig, Ingo Pill, Andrea Pferscher, Martin Tappler

*Corresponding author for this work

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
Pages (from-to)417-426
Number of pages10
JournalInnovations in Systems and Software Engineering
Volume18
Issue number3
DOIs
Publication statusPublished - Sept 2022

Keywords

  • Active automata learning
  • Model inference
  • Testing
  • Python

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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