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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+ACU, a recall value of 78.95+ACU, a combined F-measure of 62.50+ACU, 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.

-Título: Reverse engineering language product lines from existing DSL variants.-Autores: David Méndez-Acuña, José A. Galindo, Benoît Combemale, Arnaud Blouin, Benoit Baudry-Revista de publicación: Journal of Systems and Software-Volumen 133. Noviembre de 2017. Páginas 145-158-DOI: 10.1016/j.jss.2017.05.042Indicios de calidad: JCR-IF: 2,444(22/106).JCR-Q: Q1. JCR-T: T1. JCR-Category/year: COMPUTER SCIENCE, SOFTWARE ENGINEERING – 2016. 9 citas según gscholar