Exploring new directions in traceability link recovery in models: The process models case

Abstract

Traceability Links Recovery (TLR) has been a topic of interest for many years. However, TLR in Process Models has not received enough attention yet. Through this work, we study TLR between Natural Language Requirements and Process Models through three different approaches: a Models specific baseline, and two techniques based on Latent Semantic Indexing, used successfully over code. We adapted said code techniques to work for Process Models, and propose them as novel techniques for TLR in Models. The three approaches were evaluated by applying them to an academia set of Process Models, and to a set of Process Models from a real-world industrial case study. Results show that our techniques retrieve better results that the baseline Models technique in both case studies. We also studied why this is the case, and identified Process Models particularities that could potentially lead to improvement opportunities.