@inproceedings{acfab9f99b9e41e9a0898fd6dc3368d0,
title = "Learning Broadcast Protocols with LeoParDS",
abstract = "LeoParDS is a new tool for learning broadcast protocols (BPs) from a set of positive and negative example traces. It is the first tool that enables learning of a distributed computational model in a parameterized setting, i.e., with a parametric number of processes running the BP concurrently. We describe the tool along a running example, discuss some implementation details, and present experimental results on randomly generated BPs.",
keywords = "broadcast protocols, concurrent systems, learning computational models, parameterized verification",
author = "Noa Izsak and Dana Fisman and Swen Jacobs",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 22nd International Symposium on Automated Technology for Verification and Analysis, ATVA 2024 ; Conference date: 21-10-2024 Through 25-10-2024",
year = "2025",
month = jan,
day = "1",
doi = "10.1007/978-3-031-78709-6_11",
language = "American English",
isbn = "9783031787089",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "220--234",
editor = "S. Akshay and Aina Niemetz and Sriram Sankaranarayanan",
booktitle = "Automated Technology for Verification and Analysis - 22nd International Symposium, Proceedings",
address = "Germany",
}