Ontology-based NLP tool for tracing software requirements and conceptual models: an empirical study

• Published in: Requirements engineering Vol. 30; no. 3; pp. 341 - 369
• Main Authors: Mosquera, David, Ruiz, Marcela, Pastor, Oscar
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2025; Springer Nature B.V
• Subjects: Algorithms; Automation; Computer Science; Design of experiments; Effectiveness; Efficiency; Knowledge representation; Language; Links; Motivation; Natural language processing; Neural networks; Ontology; Original Article; Quality assurance; Recall; Recommender systems; Software; Software Engineering; Taxonomy; User experience; Traceability; Evaluation; NLP; Ontology; Arbitrary artefact; Trace link discovery
• ISSN: 0947-3602; 1432-010X; 1432-010X
• DOI: 10.1007/s00766-025-00447-4

Software traceability refers to maintaining, using, and generating traces among software artefacts—i.e., triples comprised of a source artefact, a target artefact, and a trace link—to support software quality assurance. Due to the effort of manually discovering trace links and the variety of software artefacts, trace link discovery tools have been proposed. Among these is OntoTraceV2.0, an ontology-based automatic reasoning and Natural Language Processing (NLP) tool for trace link discovery. In this paper, we evaluate how OntoTraceV2.0 affects subjects’ efficiency, effectiveness, and satisfaction during trace link discovery. We conducted three quasi-experiments with 70 subjects in total. We asked subjects to discover trace links between a set of semi-structured software requirements in natural language—i.e., user stories—and a conceptual model—i.e., existence dependency graphs (EDGs)—with the support of OntoTraceV2.0 and without tool support. OntoTraceV2.0 increased subjects’ median precision compared to manual trace link discovery, with an average recall decrease of 7%. Despite this, OntoTraceV2.0 enabled subjects to discover trace links 1.41–2.55 times faster, indicating a significant increase in efficiency. Moreover, OntoTraceV2.0 positively affected subjects’ perceived usefulness, while ease of use and intention to use remain areas for improvement. Qualitative feedback highlighted the need for better guidance, automation, clearer benefits of long-term traceability, and improved user experience. To improve lower recall and enhance effectiveness while improving satisfaction, we propose an improved architecture for OntoTrace. We expect our experience to allow researchers and practitioners to devise new and better tools for trace link discovery automation.