Publicación:
A Rule-Learning Approach for Detecting Faults in Highly Configurable Software Systems from Uniform Random Samples

Vista sencilla de ítem
dc.contributor.authorHeradio Gil, Rubén
dc.contributor.authorFernández Amoros, David José
dc.contributor.authorRuiz Parrado, Victoria
dc.contributor.authorCobo, Manuel J.
dc.contributor.orcidhttps://orcid.org/0000-0003-2993-7705
dc.contributor.orcidhttp://orcid.org/ 0000-0001-6575-803X
dc.coverage.spatialHawaii, USA
dc.coverage.temporal2022
dc.date.accessioned2024-10-11T11:25:56Z
dc.date.available2024-10-11T11:25:56Z
dc.date.issued2022
dc.description.abstractSoftware systems tend to become more and more configurable to satisfy the demands of their increasingly varied customers. Exhaustively testing the correctness of highly configurable software is infeasible in most cases because the space of possible configurations is typically colossal. This paper proposes addressing this challenge by (i) working with a representative sample of the configurations, i.e., a ``uniform'' random sample, and (ii) processing the results of testing the sample with a rule induction system that extracts the faults that cause the tests to fail. The paper (i) gives a concrete implementation of the approach, (ii) compares the performance of the rule learning algorithms AQ, CN2, LEM2, PART, and RIPPER, and (iii) provides empirical evidence supporting our procedureen
dc.description.versionversión final
dc.identifier.citationRuben Heradio, David Fernandez-Amoros, Victoria Ruiz-Parrado, Manuel J. Cobo. A Rule-Learning Approach for Detecting Faults in Highly Configurable Software Systems from Uniform Random Samples. Hawaii International Conference on System Sciences 2022 DOI: 0.24251/HICSS.2022.263
dc.identifier.doihttps://doi.org/10.24251/HICSS.2022.263
dc.identifier.isbn9780998133157
dc.identifier.issn1530-1605
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24024
dc.language.isoen
dc.relation.centerFacultades y escuelas
dc.relation.congressProceedings of the Annual Hawaii International Conference on System Sciences
dc.relation.departmentIngeniería de Software y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject33 Ciencias Tecnológicas::3304 Tecnología de los ordenadores
dc.titleA Rule-Learning Approach for Detecting Faults in Highly Configurable Software Systems from Uniform Random Samplesen
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
relation.isAuthorOfPublication38af03ae-439e-45a8-8383-80340d20f7cb
relation.isAuthorOfPublication60bb7374-7021-4fda-b2cb-ef7f923c64f4
relation.isAuthorOfPublication.latestForDiscovery38af03ae-439e-45a8-8383-80340d20f7cb
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Heradio_Ruben_A Rule Learning Approach for De_RUBEN HERADIO GIL.pdf
Tamaño:
755.81 KB
Formato:
Adobe Portable Document Format
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.62 KB
Formato:
Item-specific license agreed to upon submission
Descripción:
Descargar
Colecciones
Actas y comunicaciones de congresos