An NLP-Enabled Approach to Semantic Grouping for Improved Requirements Modularity and Traceability

The escalating complexity of modern software systems has rendered the management of requirements increasingly arduous, often plagued by redundancy, inconsistency, and inefficiency. Traditional manual methods prove inadequate for addressing the intricacies of dynamic, large-scale datasets. In response, this research introduces SQUIRE (Semantic Quick Requirements Engineering), a cutting-edge automated framework leveraging advanced Natural Language Processing (NLP) techniques, specifically Sentence-BERT (SBERT) embeddings and hierarchical clustering, to semantically organize requirements into coherent functional clusters. SQUIRE is meticulously designed to enhance modularity, mitigate redundancy, and strengthen traceability within requirements engineering processes. Its efficacy is rigorously validated using real-world datasets from diverse domains, including attendance management, e-commerce systems, and school operations. Empirical evaluations reveal that SQUIRE outperforms conventional clustering methods, demonstrating superior intra-cluster cohesion and inter-cluster separation, while significantly reducing manual intervention. This research establishes SQUIRE as a scalable and domain-agnostic solution, effectively addressing the evolving complexities of contemporary software development. By streamlining requirements management and enabling software teams to focus on strategic initiatives, SQUIRE advances the state of NLP-driven methodologies in Requirements Engineering, offering a robust foundation for future innovations.

@article{Izhar2025,
  title     = {An NLP-Enabled Approach to Semantic Grouping for Improved Requirements Modularity and Traceability},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Rahat Izhar and Shahid Nazir Bhatti and Sultan A. Alharthi},
  doi       = {10.14569/IJACSA.2025.0160252},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160252}
}