🤖 AI Summary
This study addresses the high complexity and labor-intensive challenges of accurately translating privacy regulations such as Brazil’s General Data Protection Law (LGPD) into actionable software requirements. It presents the first systematic exploration of leveraging large language models (LLMs) for generating LGPD-compliant requirements, proposing an automated approach that integrates legal text analysis with requirements engineering to directly map statutory provisions into user stories and acceptance test scenarios. Experimental results demonstrate that the method efficiently produces high-quality, executable compliance requirements, significantly supporting regulatory adherence during early-stage software development. This work thus offers an innovative and practical technical pathway for privacy regulation–driven requirements engineering.
📝 Abstract
Compliance with privacy legislation poses a complex challenge to Requirements Engineering (RE): translating legal norms into software requirements. In this context, this study investigates whether Large Language Models (LLMs) can simplify RE within the framework of the Brazilian General Data Protection Law (LGPD). The proposed approach utilizes current legislation to automatically generate User Stories and Acceptance Test Scenarios. The evaluation results demonstrated high performance, confirming the potential of LLMs to ensure regulatory compliance from the software's inception.