Better automated importance splitting for transient rare events

Autores
Budde, Carlos Ernesto; D'argenio, Pedro Ruben; Hartmanns, Arnd
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Statistical model checking uses simulation to overcome the state space explosion problem in formal verification. Yet its runtime explodes when faced with rare events, unless a rare event simulation method like importance splitting is used. The effectiveness of importance splitting hinges on nontrivial model-specific inputs: an importance function with matching splitting thresholds. This prevents its use by non-experts for general classes of models. In this paper, we propose new method combinations with the goal of fully automating the selection of all parameters for importance splitting. We focus on transient (reachability) properties, which particularly challenged previous techniques, and present an exhaustive practical evaluation of the new approaches on case studies from the literature. We find that using Restart simulations with a compositionally constructed importance function and thresholds determined via a new expected success method most reliably succeeds and performs very well. Our implementation within the Modest Toolset supports various classes of formal stochastic models and is publicly available.
Fil: Budde, Carlos Ernesto. Universiteit Twente; Países Bajos. Universidad Nacional de Córdoba; Argentina
Fil: D'argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Hartmanns, Arnd. Universiteit Twente; Países Bajos
Materia
Rare Event Simulation
Importance Splitting
Transient Analysis
Expected Success
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/72324
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/72324
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Better automated importance splitting for transient rare eventsBudde, Carlos ErnestoD'argenio, Pedro RubenHartmanns, ArndRare Event SimulationImportance SplittingTransient AnalysisExpected Successhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Statistical model checking uses simulation to overcome the state space explosion problem in formal verification. Yet its runtime explodes when faced with rare events, unless a rare event simulation method like importance splitting is used. The effectiveness of importance splitting hinges on nontrivial model-specific inputs: an importance function with matching splitting thresholds. This prevents its use by non-experts for general classes of models. In this paper, we propose new method combinations with the goal of fully automating the selection of all parameters for importance splitting. We focus on transient (reachability) properties, which particularly challenged previous techniques, and present an exhaustive practical evaluation of the new approaches on case studies from the literature. We find that using Restart simulations with a compositionally constructed importance function and thresholds determined via a new expected success method most reliably succeeds and performs very well. Our implementation within the Modest Toolset supports various classes of formal stochastic models and is publicly available.Fil: Budde, Carlos Ernesto. Universiteit Twente; Países Bajos. Universidad Nacional de Córdoba; ArgentinaFil: D'argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Hartmanns, Arnd. Universiteit Twente; Países BajosSpringer Verlag Berlín2017-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/72324Budde, Carlos Ernesto; D'argenio, Pedro Ruben; Hartmanns, Arnd; Better automated importance splitting for transient rare events; Springer Verlag Berlín; Lecture Notes in Computer Science; 10606 LNCS; 10-2017; 42-580302-9743CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-69483-2_3info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007%2F978-3-319-69483-2_3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:49:38Zoai:ri.conicet.gov.ar:11336/72324instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:49:39.063CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Better automated importance splitting for transient rare events
title Better automated importance splitting for transient rare events
spellingShingle Better automated importance splitting for transient rare events
Budde, Carlos Ernesto
Rare Event Simulation
Importance Splitting
Transient Analysis
Expected Success
title_short Better automated importance splitting for transient rare events
title_full Better automated importance splitting for transient rare events
title_fullStr Better automated importance splitting for transient rare events
title_full_unstemmed Better automated importance splitting for transient rare events
title_sort Better automated importance splitting for transient rare events
dc.creator.none.fl_str_mv Budde, Carlos Ernesto
D'argenio, Pedro Ruben
Hartmanns, Arnd
author Budde, Carlos Ernesto
author_facet Budde, Carlos Ernesto
D'argenio, Pedro Ruben
Hartmanns, Arnd
author_role author
author2 D'argenio, Pedro Ruben
Hartmanns, Arnd
author2_role author
author
dc.subject.none.fl_str_mv Rare Event Simulation
Importance Splitting
Transient Analysis
Expected Success
topic Rare Event Simulation
Importance Splitting
Transient Analysis
Expected Success
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Statistical model checking uses simulation to overcome the state space explosion problem in formal verification. Yet its runtime explodes when faced with rare events, unless a rare event simulation method like importance splitting is used. The effectiveness of importance splitting hinges on nontrivial model-specific inputs: an importance function with matching splitting thresholds. This prevents its use by non-experts for general classes of models. In this paper, we propose new method combinations with the goal of fully automating the selection of all parameters for importance splitting. We focus on transient (reachability) properties, which particularly challenged previous techniques, and present an exhaustive practical evaluation of the new approaches on case studies from the literature. We find that using Restart simulations with a compositionally constructed importance function and thresholds determined via a new expected success method most reliably succeeds and performs very well. Our implementation within the Modest Toolset supports various classes of formal stochastic models and is publicly available.
Fil: Budde, Carlos Ernesto. Universiteit Twente; Países Bajos. Universidad Nacional de Córdoba; Argentina
Fil: D'argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Hartmanns, Arnd. Universiteit Twente; Países Bajos
description Statistical model checking uses simulation to overcome the state space explosion problem in formal verification. Yet its runtime explodes when faced with rare events, unless a rare event simulation method like importance splitting is used. The effectiveness of importance splitting hinges on nontrivial model-specific inputs: an importance function with matching splitting thresholds. This prevents its use by non-experts for general classes of models. In this paper, we propose new method combinations with the goal of fully automating the selection of all parameters for importance splitting. We focus on transient (reachability) properties, which particularly challenged previous techniques, and present an exhaustive practical evaluation of the new approaches on case studies from the literature. We find that using Restart simulations with a compositionally constructed importance function and thresholds determined via a new expected success method most reliably succeeds and performs very well. Our implementation within the Modest Toolset supports various classes of formal stochastic models and is publicly available.
publishDate 2017
dc.date.none.fl_str_mv 2017-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/72324
Budde, Carlos Ernesto; D'argenio, Pedro Ruben; Hartmanns, Arnd; Better automated importance splitting for transient rare events; Springer Verlag Berlín; Lecture Notes in Computer Science; 10606 LNCS; 10-2017; 42-58
0302-9743
CONICET Digital
CONICET
url http://hdl.handle.net/11336/72324
identifier_str_mv Budde, Carlos Ernesto; D'argenio, Pedro Ruben; Hartmanns, Arnd; Better automated importance splitting for transient rare events; Springer Verlag Berlín; Lecture Notes in Computer Science; 10606 LNCS; 10-2017; 42-58
0302-9743
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-69483-2_3
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007%2F978-3-319-69483-2_3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Verlag Berlín
publisher.none.fl_str_mv Springer Verlag Berlín
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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