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On Implementing Symbolic Controllability

Runtime Monitors observe the execution of a system with the aim of reaching a verdict about it. One property that is expected of monitors is consistent verdict detections; this property was characterised in prior work via a symbolic analysis called symbolic controllability. This paper explores wheth...

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Detalles Bibliográficos
Autores principales: Francalanza, Adrian, Xuereb, Jasmine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282851/
http://dx.doi.org/10.1007/978-3-030-50029-0_22
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author Francalanza, Adrian
Xuereb, Jasmine
author_facet Francalanza, Adrian
Xuereb, Jasmine
author_sort Francalanza, Adrian
collection PubMed
description Runtime Monitors observe the execution of a system with the aim of reaching a verdict about it. One property that is expected of monitors is consistent verdict detections; this property was characterised in prior work via a symbolic analysis called symbolic controllability. This paper explores whether the proposed symbolic analysis lends itself well to the construction of a tool that checks monitors for this deterministic behaviour. We implement a prototype that automates this symbolic analysis, and establish complexity upper bounds for the algorithm used. We also consider a number of optimisations for the implemented prototype, and assess the potential gains against benchmark monitors.
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spelling pubmed-72828512020-06-10 On Implementing Symbolic Controllability Francalanza, Adrian Xuereb, Jasmine Coordination Models and Languages Article Runtime Monitors observe the execution of a system with the aim of reaching a verdict about it. One property that is expected of monitors is consistent verdict detections; this property was characterised in prior work via a symbolic analysis called symbolic controllability. This paper explores whether the proposed symbolic analysis lends itself well to the construction of a tool that checks monitors for this deterministic behaviour. We implement a prototype that automates this symbolic analysis, and establish complexity upper bounds for the algorithm used. We also consider a number of optimisations for the implemented prototype, and assess the potential gains against benchmark monitors. 2020-05-13 /pmc/articles/PMC7282851/ http://dx.doi.org/10.1007/978-3-030-50029-0_22 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Francalanza, Adrian
Xuereb, Jasmine
On Implementing Symbolic Controllability
title On Implementing Symbolic Controllability
title_full On Implementing Symbolic Controllability
title_fullStr On Implementing Symbolic Controllability
title_full_unstemmed On Implementing Symbolic Controllability
title_short On Implementing Symbolic Controllability
title_sort on implementing symbolic controllability
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282851/
http://dx.doi.org/10.1007/978-3-030-50029-0_22
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