Incomplete MaxSAT approaches for combinatorial testing

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dc.contributor.authorAnsótegui Gil, Carlos José
dc.contributor.authorManyà Serres, Felip
dc.contributor.authorOjeda Contreras, Jesús
dc.contributor.authorSalvia Hornos, Josep M.
dc.contributor.authorTorres, Eduard
dc.date.accessioned2022-11-07T08:35:56Z
dc.date.available2022-11-07T08:35:56Z
dc.date.issued2022
dc.description.abstractWe 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.ca_ES
dc.description.sponsorshipWe would like to thank specially Akihisa Yamada for the access to several benchmarks for our experiments and for solving some questions about his previous work on Combinatorial Testing with Constraints. This work was partially supported by Grant PID2019-109137GB-C21 funded by MCIN/AEI/10.13039/501100011033, PANDEMIES 2020 by Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR), Departament d’Empresa i Coneixement de la Generalitat de Catalunya; FONDO SUPERA COVID-19 funded by Crue-CSIC-SANTANDER, ISINC (PID2019-111544GB-C21), and the MICNN FPU fellowship (FPU18/02929).ca_ES
dc.identifier.doihttps://doi.org/10.1007/s10732-022-09495-3
dc.identifier.issn1572-9397
dc.identifier.urihttp://hdl.handle.net/10459.1/84116
dc.language.isoengca_ES
dc.publisherSpringerca_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111544GB-C21/ES/SISTEMAS DE INFERENCIA PARA INFORMACION INCONSISTENTE: FUNDAMENTOS LOGICOS/ca_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109137GB-C21/ES/SISTEMAS DE DEMOSTRACION PRACTICOS MAS ALLA DE RESOLUCION/ca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1007/s10732-022-09495-3ca_ES
dc.relation.ispartofJournal of Heuristics, 2022, vol. 28, p. 377-431ca_ES
dc.rightscc-by (c) Ansótegui et al., 2022ca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCombinatorial testingca_ES
dc.subjectMaximum satisfiabilityca_ES
dc.subjectConstraint programmingca_ES
dc.titleIncomplete MaxSAT approaches for combinatorial testingca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
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