Incomplete MaxSAT approaches for combinatorial testing

We present a Satisfiability (SAT)-based approach for building Mixed Covering Arrays with Constraints of minimum length, referred to as the Covering Array Number problem. This problem is central in Combinatorial Testing for the detection of system failures. In particular, we show how to apply Maximum...

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Detalles Bibliográficos
Autores: Carlos Ansótegui, Manyà, Felip, Josep M. Salvia, Eduard Torres
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/304463
Acceso en línea:http://hdl.handle.net/10261/304463
Access Level:acceso abierto
Palabra clave:Combinatorial testing
Maximum satisfiability
Constraint programming
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spelling Incomplete MaxSAT approaches for combinatorial testingCarlos AnsóteguiManyà, FelipJosep M. SalviaEduard TorresCombinatorial testingMaximum satisfiabilityConstraint programmingWe present a Satisfiability (SAT)-based approach for building Mixed Covering Arrays with Constraints of minimum length, referred to as the Covering Array Number problem. This problem is central in Combinatorial Testing for the detection of system failures. In particular, we show how to apply Maximum Satisfiability (MaxSAT) technology by describing efficient encodings for different classes of complete and incomplete MaxSAT solvers to compute optimal and suboptimal solutions, respectively. Similarly, we show how to solve through MaxSAT technology a closely related problem, the Tuple Number problem, which we extend to incorporate constraints. For this problem, we additionally provide a new MaxSAT-based incomplete algorithm. The extensive experimental evaluation we carry out on the available Mixed Covering Arrays with Constraints benchmarks and the comparison with state-of-the-art tools confirm the good performance of our approaches.Kluwer Academic PublishersConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2023202320222023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/304463reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1007/s10732-022-09495-3Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3044632026-05-22T06:33:51Z
dc.title.none.fl_str_mv Incomplete MaxSAT approaches for combinatorial testing
title Incomplete MaxSAT approaches for combinatorial testing
spellingShingle Incomplete MaxSAT approaches for combinatorial testing
Carlos Ansótegui
Combinatorial testing
Maximum satisfiability
Constraint programming
title_short Incomplete MaxSAT approaches for combinatorial testing
title_full Incomplete MaxSAT approaches for combinatorial testing
title_fullStr Incomplete MaxSAT approaches for combinatorial testing
title_full_unstemmed Incomplete MaxSAT approaches for combinatorial testing
title_sort Incomplete MaxSAT approaches for combinatorial testing
dc.creator.none.fl_str_mv Carlos Ansótegui
Manyà, Felip
Josep M. Salvia
Eduard Torres
author Carlos Ansótegui
author_facet Carlos Ansótegui
Manyà, Felip
Josep M. Salvia
Eduard Torres
author_role author
author2 Manyà, Felip
Josep M. Salvia
Eduard Torres
author2_role author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Combinatorial testing
Maximum satisfiability
Constraint programming
topic Combinatorial testing
Maximum satisfiability
Constraint programming
description We present a Satisfiability (SAT)-based approach for building Mixed Covering Arrays with Constraints of minimum length, referred to as the Covering Array Number problem. This problem is central in Combinatorial Testing for the detection of system failures. In particular, we show how to apply Maximum Satisfiability (MaxSAT) technology by describing efficient encodings for different classes of complete and incomplete MaxSAT solvers to compute optimal and suboptimal solutions, respectively. Similarly, we show how to solve through MaxSAT technology a closely related problem, the Tuple Number problem, which we extend to incorporate constraints. For this problem, we additionally provide a new MaxSAT-based incomplete algorithm. The extensive experimental evaluation we carry out on the available Mixed Covering Arrays with Constraints benchmarks and the comparison with state-of-the-art tools confirm the good performance of our approaches.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/304463
url http://hdl.handle.net/10261/304463
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1007/s10732-022-09495-3
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Kluwer Academic Publishers
publisher.none.fl_str_mv Kluwer Academic Publishers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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