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Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case

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Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case

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dc.contributor.author Marcén, Ana C. es_ES
dc.contributor.author Lapeña, Raúl es_ES
dc.contributor.author Pastor López, Oscar es_ES
dc.contributor.author Cetina, Carlos es_ES
dc.date.accessioned 2021-07-28T03:30:54Z
dc.date.available 2021-07-28T03:30:54Z
dc.date.issued 2020-05 es_ES
dc.identifier.issn 0164-1212 es_ES
dc.identifier.uri http://hdl.handle.net/10251/170577
dc.description.abstract [EN] Traceability Link Recovery (TLR) has been a topic of interest for many years within the software engineering community. In recent years, TLR has been attracting more attention, becoming the subject of both fundamental and applied research. However, there still exists a large gap between the actual needs of industry on one hand and the solutions published through academic research on the other. In this work, we propose a novel approach, named Evolutionary Learning to Rank for Traceability Link Recovery (TLR-ELtoR). TLR-ELtoR recovers traceability links between a requirement and a model through the combination of evolutionary computation and machine learning techniques, generating as a result a ranking of model fragments that can realize the requirement. TLR-ELtoR was evaluated in a real-world case study in the railway domain, comparing its outcomes with five TLR approaches (Information Retrieval, Linguistic Rule-based, Feedforward Neural Network, Recurrent Neural Network, and Learning to Rank). The results show that TLR-ELtoR achieved the best results for most performance indicators, providing a mean precision value of 59.91%, a recall value of 78.95%, a combined F-measure of 62.50%, and a MCC value of 0.64. The statistical analysis of the results assesses the magnitude of the improvement, and the discussion presents why TLR-ELtoR achieves better results than the baselines. es_ES
dc.description.sponsorship This work has been developed with the financial support of the Spanish State Research Agency and the Generalitat Valenciana under the projects DataME TIN2016-80811-P, ALPS RT12018-096411-B-100, ACIF/2018/171 and PROMETEO/2018/176, and co-financed with ERDF. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Journal of Systems and Software es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Traceability link recovery es_ES
dc.subject Requirements engineering es_ES
dc.subject Models es_ES
dc.subject Evolutionary algorithm es_ES
dc.subject Learning to rank es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.jss.2020.110519 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096411-B-I00/ES/ASISTENTES EVOLUTIVOS INTELIGENTES PARA INICIAR LINEAS DE PRODUCTO SOFTWARE/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2016-80811-P/ES/UN METODO DE PRODUCCION DE SOFTWARE DIRIGIDO POR MODELOS PARA EL DESARROLLO DE APLICACIONES BIG DATA/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F176/ES/GISPRO-GENOMIC INFORMATION SYSTEMS PRODUCTION/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//ACIF%2F2018%2F171/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Marcén, AC.; Lapeña, R.; Pastor López, O.; Cetina, C. (2020). Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case. Journal of Systems and Software. 163:1-24. https://doi.org/10.1016/j.jss.2020.110519 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.jss.2020.110519 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 24 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 163 es_ES
dc.relation.pasarela S\401552 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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