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dc.contributor.authorRamírez, Aurora
dc.contributor.authorFeldt, Robert
dc.contributor.authorRomero, José Raúl
dc.date.accessioned2024-01-22T15:03:30Z
dc.date.available2024-01-22T15:03:30Z
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/10396/26659
dc.description.abstractMost software companies have extensive test suites and re-run parts of them continuously to ensure recent changes have no adverse effects. Since test suites are costly to execute, industry needs methods for test case prioritisation (TCP). Recently, TCP methods use machine learning (ML) to exploit the information known about the system under test (SUT) and its test cases. However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information. This paper analyses two decades of TCP research, and presents a taxonomy of 91 information attributes that have been used. The attributes are classified with respect to their information sources and the characteristics of their extraction process. Based on this taxonomy, TCP methods validated with industrial data and those applying ML are analysed in terms of information availability, attribute combination and definition of data features suitable for ML. Relying on a high number of information attributes, assuming easy access to SUT code and simplified testing environments are identified as factors that might hamper industrial applicability of ML-based TCP. The TePIA taxonomy provides a reference framework to unify terminology and evaluate alternatives considering the cost-benefit of the information attributes.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherAssociation for Computing Machineryes_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceRamírez, A., Feldt, R., & Romero, J. R. (2023). A taxonomy of information attributes for test case prioritisation: applicability, machine learning. ACM Transactions on Software Engineering and Methodology, 32(1), 1-42. https://doi.org/10.1145/3511805es_ES
dc.subjectRegression testinges_ES
dc.subjectTaxonomyes_ES
dc.subjectMachine learninges_ES
dc.subjectTest case prioritisationes_ES
dc.subjectIndustryes_ES
dc.titleA Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1145/3511805es_ES
dc.relation.projectIDGobierno de España. PID2020-115832GB-I00es_ES
dc.relation.projectIDJunta de Andalucía. DOC_00944es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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