LLM-Driven Cost-Effective Requirements Change Impact Analysis

Abstract

Requirements are inherently subject to change throughout the software development lifecycle. Within the limited budget available to requirements engineers, manually identifying the impact of such changes on other requirements is error-prone and effort-intensive, especially in regulated domains. This can lead to overlooked impacted requirements, which, if not properly managed, can cause serious issues in downstream tasks. Inspired by the growing potential of large language models (LLMs) across diverse domains, we propose ProReFiCIA, an LLM-driven approach to automatically identify impacted requirements when changes occur. We conduct an extensive evaluation of ProReFiCIA using several LLMs and prompt variants tailored to this task. Using the best LLM-prompt combination, ProReFiCIA achieves 85.7% recall on an unseen industrial dataset, demonstrating its effectiveness in identifying impacted requirements. Further, the cost of applying ProReFiCIA remains small, as the engineer only needs to review the predicted impacted requirements, which represent 3.0% of the entire set of requirements. Lastly, incorporating domain knowledge via RAG increases recall to 95.8% while slightly raising the cost to 3.4%.