Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems

This paper presents a novel approach for augmenting proof-based verification with performance-style analysis of the kind employed in state-of-the-art model checking tools for probabilistic systems. Quantitative safety properties usually specified as probabilistic system invariants and modeled in pro...

Full description

Bibliographic Details
Main Author: Ukachukwu Ndukwu
Format: Article
Language:English
Published: Open Publishing Association 2009-12-01
Series:Electronic Proceedings in Theoretical Computer Science
Online Access:http://arxiv.org/pdf/0912.1900v1
id doaj-8b7cd338a0c74b5e87fb0af7bbb5730d
record_format Article
spelling doaj-8b7cd338a0c74b5e87fb0af7bbb5730d2020-11-24T23:55:30ZengOpen Publishing AssociationElectronic Proceedings in Theoretical Computer Science2075-21802009-12-0113Proc. QFM 2009273910.4204/EPTCS.13.3Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic SystemsUkachukwu NdukwuThis paper presents a novel approach for augmenting proof-based verification with performance-style analysis of the kind employed in state-of-the-art model checking tools for probabilistic systems. Quantitative safety properties usually specified as probabilistic system invariants and modeled in proof-based environments are evaluated using bounded model checking techniques. Our specific contributions include the statement of a theorem that is central to model checking safety properties of proof-based systems, the establishment of a procedure; and its full implementation in a prototype system (YAGA) which readily transforms a probabilistic model specified in a proof-based environment to its equivalent verifiable PRISM model equipped with reward structures. The reward structures capture the exact interpretation of the probabilistic invariants and can reveal succinct information about the model during experimental investigations. Finally, we demonstrate the novelty of the technique on a probabilistic library case study. http://arxiv.org/pdf/0912.1900v1
collection DOAJ
language English
format Article
sources DOAJ
author Ukachukwu Ndukwu
spellingShingle Ukachukwu Ndukwu
Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
Electronic Proceedings in Theoretical Computer Science
author_facet Ukachukwu Ndukwu
author_sort Ukachukwu Ndukwu
title Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
title_short Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
title_full Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
title_fullStr Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
title_full_unstemmed Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
title_sort quantitative safety: linking proof-based verification with model checking for probabilistic systems
publisher Open Publishing Association
series Electronic Proceedings in Theoretical Computer Science
issn 2075-2180
publishDate 2009-12-01
description This paper presents a novel approach for augmenting proof-based verification with performance-style analysis of the kind employed in state-of-the-art model checking tools for probabilistic systems. Quantitative safety properties usually specified as probabilistic system invariants and modeled in proof-based environments are evaluated using bounded model checking techniques. Our specific contributions include the statement of a theorem that is central to model checking safety properties of proof-based systems, the establishment of a procedure; and its full implementation in a prototype system (YAGA) which readily transforms a probabilistic model specified in a proof-based environment to its equivalent verifiable PRISM model equipped with reward structures. The reward structures capture the exact interpretation of the probabilistic invariants and can reveal succinct information about the model during experimental investigations. Finally, we demonstrate the novelty of the technique on a probabilistic library case study.
url http://arxiv.org/pdf/0912.1900v1
work_keys_str_mv AT ukachukwundukwu quantitativesafetylinkingproofbasedverificationwithmodelcheckingforprobabilisticsystems
_version_ 1725462180801806336