dc.creator | Temple, Paul | es |
dc.creator | Galindo Duarte, José Ángel | es |
dc.creator | Acher, Mathieu | es |
dc.creator | Jézéquel, Jean-Marc | es |
dc.date.accessioned | 2017-07-24T08:56:49Z | |
dc.date.available | 2017-07-24T08:56:49Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Temple, P., Galindo Duarte, J.Á., Acher, M. y Jézéquel, J. (2016). Using Machine Learning to Infer Constraints for Product Lines. En SPLC 2016 : 20th International Systems and Software Product Line Conference (209-218), Beijing, China: ACM. | |
dc.identifier.uri | http://hdl.handle.net/11441/62946 | |
dc.description.abstract | Variability intensive systems may include several thousand
features allowing for an enormous number of possible configurations,
including wrong ones (e.g. the derived product
does not compile). For years, engineers have been using constraints
to a priori restrict the space of possible configurations,
i.e. to exclude configurations that would violate these
constraints. The challenge is to find the set of constraints
that would be both precise (allow all correct configurations)
and complete (never allow a wrong configuration with respect
to some oracle). In this paper, we propose the use of a
machine learning approach to infer such product-line
constraints from an oracle that is able to assess whether a
given product is correct. We propose to randomly generate
products from the product line, keeping for each of them its
resolution model. Then we classify these products accord-ing
to the oracle, and use their resolution models to infer crosstree
constraints over the product-line. We validate our
approach on a product-line video generator, using a simple
computer vision algorithm as an oracle. We show that an interesting
set of cross-tree constraint can be generated, with
reasonable precision and recall. | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | ACM | es |
dc.relation.ispartof | SPLC 2016 : 20th International Systems and Software Product Line Conference (2016), p 209-218 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Software Product Lines | es |
dc.subject | Machine learning | es |
dc.subject | Constraints and variability mining | es |
dc.subject | Software testing | es |
dc.subject | Variability Modeling | es |
dc.title | Using Machine Learning to Infer Constraints for Product Lines | 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.publisherversion | http://dl.acm.org/citation.cfm?id=2934472 | es |
dc.identifier.doi | 10.1145/2934466.2934472 | es |
idus.format.extent | 10 | es |
dc.publication.initialPage | 209 | es |
dc.publication.endPage | 218 | es |
dc.eventtitle | SPLC 2016 : 20th International Systems and Software Product Line Conference | es |
dc.eventinstitution | Beijing, China | es |
dc.relation.publicationplace | New York, USA | es |