AALpy: An Active Automata Learning Library

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-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 manuscript, we present AALpy’s core functionalities, illustrate its usage via examples, and evaluate its learning performance.
Original languageEnglish
Title of host publicationAutomated Technology for Verification and Analysis - ATVA 2021
EditorsZhe Hou, Vijay Ganesh
Place of PublicationCham
PublisherSpringer
Pages67-73
Number of pages7
ISBN (Electronic)978-3-030-88885-5
ISBN (Print)978-3-030-88884-8
DOIs
Publication statusPublished - 2021
Event19th International Symposium on Automated Technology for Verification and Analysis : ATVA 2021 - Virtuell, Australia
Duration: 18 Oct 202122 Oct 2021

Publication series

NameLecture Notes in Computer Science
Volume12971
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Symposium on Automated Technology for Verification and Analysis
Abbreviated titleATVA 2021
Country/TerritoryAustralia
CityVirtuell
Period18/10/2122/10/21

Keywords

  • Active automata learning
  • Model inference
  • Python

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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