arXiv:2610.33920v1 Announce Type: new Abstract: Change is inevitable in software systems, where requirements must continuously adapt to evolving stakeholder needs, regulations, and project scopes. Managing such changes is a fundamental software engineering challenge, as manually identifying the impact of a change request, given high requirements volume, Natural Language (NL) complexity, and intricate interdependencies, is error-prone and time-consuming. Large Language Models (LLMs) offer promising solutions for requirements change impact analysis (CIA), yet their effectiveness when enhanced with advanced techniques remains underexplored. We present DRILS, an LLM-based agentic framework for requirements Change Impact Analysis, employing a multi-agent architecture to generate candidate impact sets, with an LLM-as-a-judge promoting diversity during selection. DRILS features two key components: RE-RAG, a carefully engineered RAG technique providing focused domain context, and a requirement dependency graph (RDG) as an additional knowledge source. Evaluation across three manually curated datasets demonstrates superior performance over state-of-the-art baselines, achieving an average recall of 87.1%, precision of 76.3%, and F1 score of 81% at a lower cost. Precision gains are particularly significant, 41.1%, 83.4%, and 149.8% over the closest baseline per dataset, as higher precision directly reduces false positives and manual validation effort. We also contribute three annotated CIA datasets to support future research.
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