DRILS: Dependency-based Requirement Change Impact Analysis Using LLMs

Romina Etezadi, Ikram Darif, Lionel Briand, Ramesh S, Parkish Peranandam

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…