Mostrar el registro sencillo del ítem

Artículo

dc.creatorRamos Gutiérrez, Belénes
dc.creatorVarela Vaca, Ángel Jesúses
dc.creatorGalindo Duarte, José Ángeles
dc.creatorGómez López, María Teresaes
dc.creatorBenavides Cuevas, David Felipees
dc.date.accessioned2021-07-01T10:39:00Z
dc.date.available2021-07-01T10:39:00Z
dc.date.issued2021
dc.identifier.citationRamos Gutiérrez, B., Varela Vaca, Á.J., Galindo Duarte, J.Á., Gómez López, M.T. y Benavides Cuevas, D.F. (2021). Discovering configuration workflows from existing logs using process mining. Empirical Software Engineering, 26 (1)
dc.identifier.issn1382-3256es
dc.identifier.urihttps://hdl.handle.net/11441/115013
dc.description.abstractVariability models are used to build configurators, for guiding users through the configuration process to reach the desired setting that fulfils user requirements. The same variability model can be used to design different configurators employing different techniques. One of the design options that can change in a configurator is the configuration workflow, i.e., the order and sequence in which the different configuration elements are presented to the configuration stakeholders. When developing a configurator, a challenge is to decide the configuration workflow that better suits stakeholders according to previous configurations. For example, when configuring a Linux distribution the configuration process starts by choosing the network or the graphic card and then, other packages concerning a given sequence. In this paper, we present COnfiguration workfLOw proceSS mIning (COLOSSI), a framework that can automatically assist determining the configuration workflow that better fits the configuration logs generated by user activities given a set of logs of previous configurations and a variability model. COLOSSI is based on process discovery, commonly used in the process mining area, with an adaptation to configuration contexts. Derived from the possible complexity of both logs and the discovered processes, often, it is necessary to divide the traces into small ones. This provides an easier configuration workflow to be understood and followed by the user during the configuration process. In this paper, we apply and compare four different techniques for the traces clustering: greedy, backtracking, genetic and hierarchical algorithms. Our proposal is validated in three different scenarios, to show its feasibility, an ERP configuration, a Smart Farming, and a Computer Configuration. Furthermore, we open the door to new applications of process mining techniques in different areas of software product line engineering along with the necessity to apply clustering techniques for the trace preparation in the context of configuration workflows.es
dc.description.sponsorshipMinisterio de Ciencia y Tecnología RTI2018-094283-B-C33es
dc.description.sponsorshipMinisterio de Ciencia y Tecnología RTI2018-101204-B-C22es
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades MCIU-AEI TIN2017-90644-REDT)es
dc.formatapplication/pdfes
dc.format.extent41es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofEmpirical Software Engineering, 26 (1)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectVariabilityes
dc.subjectConfiguration workflowes
dc.subjectProcess mininges
dc.subjectProcess discoveryes
dc.subjectClusteringes
dc.titleDiscovering configuration workflows from existing logs using process mininges
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDRTI2018-094283-B-C33es
dc.relation.projectIDRTI2018-101204-B-C22es
dc.relation.projectIDMCIU-AEI TIN2017-90644-REDT)es
dc.relation.publisherversionhttps://link.springer.com/journal/10664/volumes-and-issues/26-1es
dc.identifier.doi10.1007/s10664-020-09911-xes
dc.journaltitleEmpirical Software Engineeringes
dc.publication.volumen26es
dc.publication.issue1es
dc.contributor.funderMinisterio de Ciencia Y Tecnología (MCYT). Españaes
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (MICINN). Españaes

FicherosTamañoFormatoVerDescripción
Discovering configuration workflows ...2.833MbIcon   [PDF] Ver/Abrir  

Este registro aparece en las siguientes colecciones

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como: Attribution-NonCommercial-NoDerivatives 4.0 Internacional