Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case Abstract   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 (TLRELtoR). TLRELtoR 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.  TLRELtoR was evaluated in a realworld case study in the railway domain, comparing its outcomes with five TLR approaches (Information Retrieval, Linguistic Rulebased, Feedforward Neural Network, Recurrent Neural Network, and Learning to Rank). The results show that TLRELtoR achieved the best results for most performance indicators, providing a mean precision value of 59.91%, a recall value of 78.95%, a combined Fmeasure 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 TLRELtoR achieves better results than the baselines.

Automated reasoning framework for traceability management of system of systems Abstract   An important aspect in system of systems (SoS) is the realization of the capabilities in different systems that work together. Identifying and locating these capabilities are important to orchestrate the overall activities and hereby to achieve the overall goal of the SoS. System elements and capabilities in SoS however, are rarely stable and need to evolve in different ways and different times in accordance with the changing requirements. To manage the SoS and cope with its evolution it is necessary that the dependency links to the capabilities and the system elements can be easily traced. Several approaches have been proposed to model traceability and reason about these by extending a predetermined set of possible trace links with fixed semantics. However, for the context of SoS a fixed traceability model with fixed traceability semantics is limited to consider the various different and changing scenarios. In this article, we first present the different traceability requirements for managing traceability in the context of SoS. Subsequently, we present the metamodel and the corresponding domain specific language to support modeling traceability and traceability analysis approaches within the evolving SoS context. Further, we provide the tool support for automated reasoning of traceability of SoS capabilities and system elements. We illustrate and discuss the approach for the application to a smart city SoS.