dc.creator | Horcas Aguilera, José Miguel | es |
dc.creator | Galindo Duarte, José Ángel | es |
dc.creator | Heradio, Ruben | es |
dc.creator | Fernández Amorós, David | es |
dc.creator | Benavides Cuevas, David Felipe | es |
dc.date.accessioned | 2022-04-11T10:32:35Z | |
dc.date.available | 2022-04-11T10:32:35Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Horcas Aguilera, J.M., Galindo Duarte, J.Á., Heradio, R., Fernández Amorós, D. y Benavides Cuevas, D.F. (2021). Monte Carlo Tree Search for Feature Model Analyses: a General Framework for Decision-Making. En SPLC 2021: 25th International Systems and Software Product Line Conference (190-201), Leicester, United Kindom: Association for Computing Machinery (ACM). | |
dc.identifier.isbn | 978-1-4503-8469-8 | es |
dc.identifier.uri | https://hdl.handle.net/11441/132013 | |
dc.description.abstract | The colossal solution spaces of most configurable systems make
intractable their exhaustive exploration. Accordingly, relevant
anal-yses remain open research problems. There exist analyses
alterna-tives such as SAT solving or constraint programming.
However, none of them have explored simulation-based
methods. Monte Carlo-based decision making is a simulation based method for deal-ing with colossal solution spaces using
randomness. This paper proposes a conceptual framework that
tackles various of those anal-yses using Monte Carlo methods,
which have proven to succeed in vast search spaces (e.g., game
theory). Our general framework is described formally, and its
flexibility to cope with a diversity of analysis problemsis discussed
(e.g., finding defective configurations, feature model reverse
engineering or getting optimal performance configurations).
Additionally, we present a Python implementation of the
framework that shows the feasibility of our proposal. With this
contribution, we envision that different problems can be ad dressed using Monte Carlo simulations and that our framework
can be used to advance the state of the art a step forward. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad RTI2018-101204-B-C22 (OPHELIA) | es |
dc.format | application/pdf | es |
dc.format.extent | 12 | es |
dc.language.iso | eng | es |
dc.publisher | Association for Computing Machinery (ACM) | es |
dc.relation.ispartof | SPLC 2021: 25th International Systems and Software Product Line Conference (2021), pp. 190-201. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Configurable Systems | es |
dc.subject | Variability modeling | es |
dc.subject | Feature models | es |
dc.subject | Monte Carlo tree search | es |
dc.subject | Software product lines | es |
dc.title | Monte Carlo Tree Search for Feature Model Analyses: a General Framework for Decision-Making | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | RTI2018-101204-B-C22 (OPHELIA) | es |
dc.relation.publisherversion | https://dl.acm.org/doi/10.1145/3461001.3471146 | es |
dc.identifier.doi | 10.1145/3461001.3471146 | es |
dc.contributor.group | Universidad de Sevilla. TIC258: Data-centric Computing Research Hub | es |
dc.publication.initialPage | 190 | es |
dc.publication.endPage | 201 | es |
dc.eventtitle | SPLC 2021: 25th International Systems and Software Product Line Conference | es |
dc.eventinstitution | Leicester, United Kindom | es |
dc.relation.publicationplace | New York, USA | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |