A Graph-Based Model for Automatic Test Case Generation from Textual Requirements with Hierarchical Coverage
The automation of software test case generation from natural language requirements remains a critical challenge in software engineering. While large language models (LLMs) demonstrate impressive generation capabilities, they suffer from high discrepancy rates (up to 57% for direct generation), hallucinated test steps, and lack formal verification mechanisms for safety-critical constraints. This paper presents a novel algorithmic framework that addresses these limitations through five principal contributions. First, we introduce the Neuro-Symbolic Requirements Graph (