research-article

GreASE: A Tool for Efficient “Nonequivalence” Checking

Authors:

Nicoletta de Francesco,

Giuseppe Lettieri,

Antonella Santone,

Gigliola VagliniAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 23, Issue 3

Article No.: 24, Pages 1 - 26

https://doi.org/10.1145/2560563

Published: 02 June 2014 Publication History

Abstract

Equivalence checking plays a crucial role in formal verification to ensure the correctness of concurrent systems. However, this method cannot be scaled as easily with the increasing complexity of systems due to the state explosion problem. This article presents an efficient procedure, based on heuristic search, for checking Milner's strong and weak equivalence; to achieve higher efficiency, we actually search for a difference between two processes to be discovered as soon as possible, thus the heuristics aims to find a counterexample, even if not the minimum one, to prove nonequivalence. The presented algorithm builds the system state graph on-the-fly, during the checking, and the heuristics promotes the construction of the more promising subgraph. The heuristic function is syntax based, but the approach can be applied to different specification languages such as CCS, LOTOS, and CSP, provided that the language semantics is based on the concept of transition. The algorithm to explore the search space of the problem is based on a greedy technique; GreASE (Greedy Algorithm for System Equivalence), the tool supporting the approach, is used to evaluate the achieved reduction of both state-space size and time with respect to other verification environments.

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GreASE: A Tool for Efficient “Nonequivalence” Checking

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Published In

cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 23, Issue 3

May 2014

230 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/2628068

Issue’s Table of Contents

Copyright © 2014 ACM.

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Publication History

Published: 02 June 2014

Accepted: 01 December 2013

Revised: 01 September 2013

Received: 01 March 2013

Published in TOSEM Volume 23, Issue 3

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https://doi.org/10.1109/COMPSAC57700.2023.00237
Casale RVarriano GSantone AMessina CCasale CGitto SSconfienza LBali MBrunese L(2023)Predicting risk of metastases and recurrence in soft-tissue sarcomas via Radiomics and Formal MethodsJAMIA Open10.1093/jamiaopen/ooad0256:2Online publication date: 12-Apr-2023
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Mercaldo FBrunese MMerolla FRocca AZappia MSantone A(2022)Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic FeaturesApplied Sciences10.3390/app12231190012:23(11900)Online publication date: 22-Nov-2022
https://doi.org/10.3390/app122311900
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