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A Monte Carlo tree search conceptual framework for feature model analyses

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dc.contributor.authorHorcas, José Miguel
dc.contributor.authorGalindo, José A.
dc.contributor.authorBenavides, David
dc.contributor.authorHeradio Gil, Rubén
dc.contributor.authorFernández Amoros, David José
dc.date.accessioned2024-06-11T15:15:20Z
dc.date.available2024-06-11T15:15:20Z
dc.date.issued2023-01
dc.description.abstractChallenging domains of the future such as Smart Cities, Cloud Computing, or Industry 4.0 expose highly variable systems with colossal configuration spaces. The automated analysis of those systems’ variability has often relied on SAT solving and constraint programming. However, many of the analyses have to deal with the uncertainty introduced by the fact that undertaking an exhaustive exploration of the whole configuration space is usually intractable. In addition, not all analyses need to deal with the configuration space of the feature models, but with different search spaces where analyses are performed over the structure of the feature models, the constraints, or the implementation artifacts, instead of configurations. This paper proposes a conceptual framework that tackles various of those analyses using Monte Carlo tree search methods, which have proven to succeed in vast search spaces (e.g., game theory, scheduling tasks, security, program synthesis, etc.). Our general framework is formally described, and its flexibility to cope with a diversity of analysis problems is discussed. We provide a Python implementation of the framework that shows the feasibility of our proposal, identifying up to 11 lessons learned, and open challenges about the usage of the Monte Carlo methods in the software product line context. With this contribution, we envision that different problems can be addressed using Monte Carlo simulations and that our framework can be used to advance the state-of-the-art one step forward.en
dc.description.versionversión original
dc.identifier.doihttps://doi.org/10.1016/j.jss.2022.111551
dc.identifier.issn0164-1212; eISSN: 1873-1228
dc.identifier.urihttps://hdl.handle.net/20.500.14468/22383
dc.journal.titleJournal of Systems and Software
dc.journal.volume195
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentIngeniería de Software y Sistemas Informáticos
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject.keywordsautomated analysis
dc.subject.keywordsconfigurable systems
dc.subject.keywordsfeature models
dc.subject.keywordsMonte Carlo tree search
dc.subject.keywordssoftware product lines
dc.subject.keywordsvariability
dc.titleA Monte Carlo tree search conceptual framework for feature model analyseses
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication38af03ae-439e-45a8-8383-80340d20f7cb
relation.isAuthorOfPublication60bb7374-7021-4fda-b2cb-ef7f923c64f4
relation.isAuthorOfPublication.latestForDiscovery38af03ae-439e-45a8-8383-80340d20f7cb
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