Leveraging execution traces to enhance traceability links recovery in BPMN models

Traceability Links Recovery has been a topic of interest for many years, resulting in techniques that perform traceability based on the linguistic clues of the software artifacts under study. However, BPMN models tend to present an overall lack of linguistic clues when compared to code-based artifacts or code generation models. Hence, TLR becomes a harder task when performed among requirements and BPMN models. This paper proposes a novel approach, called METRA, that leverages the execution traces of BPMN to expand the BPMN models. The expansion of the BPMN models enhances their linguistic clues, bridging the language between BPMN models and other software artifacts, and improving the TLR process between requirements and BPMN models. The proposed approach is evaluated through a real-world industrial case study, comparing its outcomes against two state-of-the-art baselines, TLR and LORE. The paper also evaluates the combination of METRA with LORE against the rest of the approaches, including standalone METRA. The evaluation process generates a report of measurements (precision, recall, f-measure, and MCC), over which a statistical analysis is conducted. Results show that approaches based on METRA maintain the excellent precision results obtained by baseline approaches (74.2% for METRA, 78.8% for METRA+LORE), whilst also improving the recall results from the unacceptable values obtained by the baselines to good values (72.4% for METRA, 73.9% for METRA+LORE). Moreover, according to the statistical analysis, the differences in the results obtained by the evaluated approaches are statistically significant. This paper opens a novel field of work in TLR by analyzing the improvement of the TLR process through the inclusion of linguistic clues present in execution traces, and discusses ideas for further research that can delve into this promising direction explored by our work.