Automated Generation of Test Scenarios from Natural Language Requirements using Retrieval-Augmented Large Language Models: An Industrial Evaluation

This paper presents an automated approach (RAGTAG) for generating test scenarios from natural language requirements using Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs). The approach leverages the integration of domain knowledge with LLMs' generation capabilities to produce accurate and relevant test scenarios.