Graph Neural Networks for Software Dependency Discovery

Abstract

This paper explores the application of Graph Neural Networks (GNNs) for automated software dependency discovery. Traditional methods for identifying dependencies rely heavily on manual annotation or rule-based approaches, often proving insufficient in complex software systems. We propose a novel approach that leverages the inherent structure of software code by representing it as a directed acyclic graph (DAG). GNNs, particularly Graph Convolutional Networks (GCNs), are then employed to learn dependencies directly from these graph representations. This allows for a more nuanced understanding of code relationships and significantly improves the accuracy and scalability of dependency discovery compared to existing techniques. We demonstrate the effectiveness of this approach through a theoretical framework, outlining the key components and their interactions. The core claim is that utilizing GNNs to learn dependencies within software code enables automatic and intelligent dependency discovery. The core mechanism involves mapping software code to DAGs and utilizing GNNs to model the relationships between nodes. This represents a significant advancement in the field, offering a more robust and adaptable solution for understanding software ecosystems.