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dc.contributor.authorArrieta, Aitor
dc.contributor.authorValle Entrena, Pablo
dc.contributor.authorAgirre, Joseba Andoni
dc.contributor.authorSagardui, Goiuria
dc.date.accessioned2023-01-13T10:47:08Z
dc.date.available2023-01-13T10:47:08Z
dc.date.issued2022
dc.identifier.issn1557-7392en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=170556en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5949
dc.description.abstractThe time it takes software systems to be tested is usually long. Search-based test selection has been a widely investigated technique to optimize the testing process. In this paper, we propose a set of seeding strategies for the test case selection problem that generate the initial population of pareto-based multi-objective algorithms, with the goals of (1) helping to find an overall better set of solutions and (2) enhancing the convergence of the algorithms. The seeding strategies were integrated with four state-of-the-art multi-objective search algorithms and applied into two contexts where regression-testing is paramount: (1) Simulation-based testing of Cyber-Physical Systems and (2) Continuous Integration. For the first context, we evaluated our approach by using six fitness function combinations and six independent case studies, whereas in the second context we derived a total of six fitness function combinations and employed four case studies. Our evaluation suggests that some of the proposed seeding strategies are indeed helpful for solving the multi-objective test case selection problem. Specifically, the proposed seeding strategies provided a higher convergence of the algorithms towards optimal solutions in 96% of the studied scenarios and an overall cost-effectiveness with a standard search budget in 85% of the studied scenarios.en
dc.description.sponsorshipGobierno Vasco-Eusko Jaurlaritzaes
dc.language.isoengen
dc.publisherACMen
dc.rights© 2022 Association for Computing Machineryen
dc.subjectTest Case Selectionen
dc.subjectSearch-based Software Testingen
dc.subjectRegression Testingen
dc.titleSome Seeds are Strong : Seeding Strategies for Search-based Test Case Selectionen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceACM Transactions on Software Engineering and Methodology.en
local.contributor.groupIngeniería del software y sistemases
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1145/3532182en
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Ikertalde Convocatoria 2019-2021/IT1326-19/CAPV/Ingeniería de Software y Sistemas/en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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