Automated Verification of Formal Code Specifications using Graph Neural Networks and Multi-Modal Data Fusion

**Abstract:** This paper introduces a novel framework for automated verification of formal code specifications employing graph neural networks (GNNs) and multi-modal data fusion. Existing code verification tools often struggle with complex specifications and require significant manual effort. Our approach integrates semantic analysis, structural decomposition, and dynamic execution traces into a unified GNN architecture, enabling efficient verification of large and complex codebases. We demonstrate the feasibility and efficacy of this approach through comprehensive experimentation, achieving higher accuracy and reduced verification time compared to existing techniques, with immediate applicability to safety-critical systems and large-scale software development projects.