Compositional Learning for Interleaving Parallel Automata

TY - CHAP

T1 - Compositional Learning for Interleaving Parallel Automata

AU - Labbaf, Faezeh

AU - Groote, Jan Friso

AU - Hojjat, Hossein

AU - Mousavi, Mohammad Reza

N1 - Funding Information: We would like to thank Rasta Tadayon and Amin Asadi Sarijalou for their contributions to the early stages of this work. The work of Mohammad Reza Mousavi was supported by the UKRI Trustworthy Autonomous Systems Node in Verifiability, Grant Award Reference EP/V026801/2. We thank the reviewers of FOSSACS for their insightful and constructive comments, which, in our view, led to improvements in our final paper. We thank the Artifact Evaluation committee at ESOP/FOSSACS for their careful review of our lab package. Publisher Copyright: © 2023, The Author(s).

PY - 2023

Y1 - 2023

N2 - Active automata learning has been a successful technique to learn the behaviourof state-based systems by interacting with them through queries. In this paper, we develop a compositional algorithm for active automata learning in which systems comprising interleaving parallel components are learned compositionally. Our algorithm automatically learns the structure of systems while learning the behaviour of the components. We prove that our approach is sound and that it learns a maximal set of interleaving parallel components. We empirically evaluate the effectiveness of our approach and show that our approach requires significantly fewer numbers of input symbols and resets while learning systems. Our empirical evaluation is based on a large number of subject systems obtained from a case study in the automotive domain.

AB - Active automata learning has been a successful technique to learn the behaviourof state-based systems by interacting with them through queries. In this paper, we develop a compositional algorithm for active automata learning in which systems comprising interleaving parallel components are learned compositionally. Our algorithm automatically learns the structure of systems while learning the behaviour of the components. We prove that our approach is sound and that it learns a maximal set of interleaving parallel components. We empirically evaluate the effectiveness of our approach and show that our approach requires significantly fewer numbers of input symbols and resets while learning systems. Our empirical evaluation is based on a large number of subject systems obtained from a case study in the automotive domain.

UR - http://www.scopus.com/inward/record.url?scp=85161380805&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-30829-1_20

DO - 10.1007/978-3-031-30829-1_20

M3 - Conference paper

SN - 9783031308284

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 413

EP - 435

BT - Proceedings of the 26th International Conference on Foundations of Software Science and Computation Structures (FoSSaCS 2023)

A2 - Kupferman, Orna

A2 - Sobocinski, Pawel

PB - Springer

ER -