[Context & Motivation] Requirements Traceability (RT) aims to follow and describe the lifecycle of a requirement. RT is employed either because it is mandated, or because the product team perceives benefits. [Problem] Many RT practices, including the establishment and maintenance of trace links, are generally carried out manually, thereby being prone to mistakes, vulnerable to changes, time-consuming, and difficult to maintain. Automated tracing tools have been proposed; yet, their adoption is still low, often because of the limited evidence of their effectiveness. [Results] We present the design of a tracing tool for automatically recovering traces between JIRA issues (representing user stories and bugs) and commits in a model-driven development (MDD) context. We employed existing literature and, using process and text-based data, we created 123 features to train a machine learning (ML) classifier. This classifier was validated using three real MDD industry datasets. For a trace recommendation sce-nario, we were able to obtain an average F2-score of 69% with the best tested configuration. For an automated trace maintenance scenario, an F0.5-score of 76% was obtained. [Contribution] Our findings provide insights on the effectiveness of state-of-the-art trace recovery techniques in an MDD context by us-ing real-world data from a large company in the field of low-code development.