A STRUCTURED TRACEABILITY APPROACH FOR TRANSFORMING REQUIREMENTS INTO CLASS DIAGRAMS

In software engineering, requirement traceability is a critical factor in ensuring that software systems comply with user requirements, enabling effective management and verification throughout the development lifecycle. However, due to inherent ambiguities and inconsistencies in user-specified requirements, typically expressed in natural language, establishing accurate traceability remains a significant challenge. This study introduces a novel, automated approach to requirement traceability, addressing this challenge by transforming natural language requirement specifications (NLRs) into class diagrams while simultaneously generating traceability links. The proposed approach leverages Natural Language Processing (NLP) techniques and Semantic Object Models (SOMs), a structured and reusable pattern-based method that enhances the accuracy and consistency of traceability links. To validate this approach, the TRAM application was developed and evaluated against manually produced class diagrams from requirements engineering experts. Precision and recall metrics were used to assess the accuracy and completeness of the generated traceability links. Results indicate that TRAM achieves high recall (0.60–0.96), demonstrating its effectiveness in capturing relevant elements, although precision (0.25–0.51) remains a challenge due to the integration of predefined elements through SOM patterns. These findings highlight the contribution of this research in advancing automated traceability solutions, reducing manual effort, and improving consistency in requirements engineering. Additionally, the structured use of SOMs suggests that this methodology can be extended beyond class diagrams to other software artifacts, broadening its applicability in software engineering.