Interpolation-Based Learning for Bounded Model Checking

Anissa Kheireddine, Etienne Renault, Souheib Baarir, Souheib Baarir

2024

Abstract

In this paper, we propose an interpolation-based learning approach to enhance the effectiveness of solving the bounded model checking problem. Our method involves breaking down the formula into partitions, where these partitions interact through a reconciliation scheme leveraging the power of the interpolation theorem to derive relevant information. Our approach can seamlessly serve two primary purposes: (1) as a preprocessing engine in sequential contexts or (2) as part of a parallel framework within a portfolio of CDCL solvers.

Paper Citation

in Harvard Style

Kheireddine A., Renault E. and Baarir S. (2024). Interpolation-Based Learning for Bounded Model Checking. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-696-5, SciTePress, pages 605-614. DOI: 10.5220/0012703500003687

in Bibtex Style

@conference{enase24,
author={Anissa Kheireddine and Etienne Renault and Souheib Baarir},
title={Interpolation-Based Learning for Bounded Model Checking},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2024},
pages={605-614},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012703500003687},
isbn={978-989-758-696-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Interpolation-Based Learning for Bounded Model Checking
SN - 978-989-758-696-5
AU - Kheireddine A.
AU - Renault E.
AU - Baarir S.
PY - 2024
SP - 605
EP - 614
DO - 10.5220/0012703500003687
PB - SciTePress