Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train Control and Management Case

• Autores: Ana Cristina Marcén Terraza, Raúl Lapeña, Oscar Pastor López, Carlos Cetina Englada
• Localización: Actas de las XXV Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2021): [Málaga, 22 al 24 de septiembre de 2021] / coord. por Rafael Capilla Sevilla, Maider Azanza Sese, Miguel Rodríguez Luaces, M. M. Roldán García, Dolores Burgueño Caballero, José Raúl Romero Salguero, José Antonio Parejo Maestre, José Francisco Chicano García, Marcela Genero, Óscar Díaz García, José González Enríquez, María Carmen Penades Gramage; Silvia Mara Abrahao Gonzales (col.), 2021
• Idioma: inglés
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• Resumen

  • 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.