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dc.contributor.authorIovino, Ludovico
dc.contributor.authorBarriga Rodriguez, Angela
dc.contributor.authorRutle, Adrian
dc.contributor.authorHeldal, Rogardt
dc.date.accessioned2021-04-12T07:41:21Z
dc.date.available2021-04-12T07:41:21Z
dc.date.created2020-12-17T14:36:29Z
dc.date.issued2020
dc.identifier.citationLudovico, I., Barriga, A., Rutle, A., & Heldal, R. (2020). Model Repair with Quality-Based Reinforcement Learning. The Journal of Object Technology, 19(2).en_US
dc.identifier.issn1660-1769
dc.identifier.urihttps://hdl.handle.net/11250/2737208
dc.description.abstractDomain modeling is a core activity in Model-Driven Engineering, and these models must be correct. A large number of artifacts may be constructed on top of these domain models, such as instance models, transformations, and editors. Similar to any other software artifact, domain models are subject to the introduction of errors during the modeling process. There are a number of existing tools that reduce the burden of manually dealing with correctness issues in models. Although various approaches have been proposed to support the quality assessment of modeling artifacts in the past decade, the quality of the automatically repaired models has not been the focus of repairing processes. In this paper, we propose the integration of an automatic evaluation of domain models based on a quality model with a framework for personalized and automatic model repair. The framework uses reinforcement learning to find the best sequence of actions for repairing a broken model.en_US
dc.language.isoengen_US
dc.publisherAITO — Association Internationale pour les Technologies Objetsen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleModel Repair with Quality-Based Reinforcement Learningen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2020 The Author(s)en_US
dc.source.volume19en_US
dc.source.journalThe Journal of Object Technologyen_US
dc.source.issue2en_US
dc.identifier.doi10.5381/JOT.2020.19.2.A17
dc.identifier.cristin1861142
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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