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<p>Effective Requirements Engineering (RE) is essential for building successful software systems, yet analyzing unstructured stakeholder input remains a persistent challenge. Qualitative Data Analysis (QDA) provides structured methods, open coding (entity extraction), axial coding (relationship discovery), and selective coding (model refinement) to transform natural language requirements into domain models. While manual QDA has proven effective for requirements analysis, it remains time-consuming, repetitive, and difficult to scale. Although individual RE tasks have been automated, no prior work has automated the complete QDA methodology for domain modeling. In this paper, we present QuaRUM, the first framework to automate end-to-end QDA for UML domain model generation by combining large language models with retrieval-augmented generation. QuaRUM processes requirements through document ingestion, semantic indexing, and retrieval-augmented coding, and helps ground each model element in the source text to mitigate hallucination risks. Empirical results show that QuaRUM performs with high accuracy across three domains. It achieves F1-scores between 0.85 and 0.98. Cohen’s <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\kappa\)</EquationSource> </InlineEquation> reaches up to 0.92, surpassing human inter-coder agreement. Notably, QuaRUM recovers 37 valid attributes and 23 relationships initially missed by human analysts. A cost-benefit analysis shows a 218% Return on Investment (ROI) for initial use, increasing to 1,131% in repeated deployments, demonstrating strong economic scalability.</p>

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QuaRUM: qualitative data analysis-based retrieval-augmented UML domain model from requirements documents

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Syed Tauhid Ullah Shah,
  • Mohamad Hussein,
  • Ann Barcomb,
  • Mohammad Moshirpour

摘要

Effective Requirements Engineering (RE) is essential for building successful software systems, yet analyzing unstructured stakeholder input remains a persistent challenge. Qualitative Data Analysis (QDA) provides structured methods, open coding (entity extraction), axial coding (relationship discovery), and selective coding (model refinement) to transform natural language requirements into domain models. While manual QDA has proven effective for requirements analysis, it remains time-consuming, repetitive, and difficult to scale. Although individual RE tasks have been automated, no prior work has automated the complete QDA methodology for domain modeling. In this paper, we present QuaRUM, the first framework to automate end-to-end QDA for UML domain model generation by combining large language models with retrieval-augmented generation. QuaRUM processes requirements through document ingestion, semantic indexing, and retrieval-augmented coding, and helps ground each model element in the source text to mitigate hallucination risks. Empirical results show that QuaRUM performs with high accuracy across three domains. It achieves F1-scores between 0.85 and 0.98. Cohen’s \(\kappa\) reaches up to 0.92, surpassing human inter-coder agreement. Notably, QuaRUM recovers 37 valid attributes and 23 relationships initially missed by human analysts. A cost-benefit analysis shows a 218% Return on Investment (ROI) for initial use, increasing to 1,131% in repeated deployments, demonstrating strong economic scalability.