Dependency Discovery Based on Topological Information

Abstract

This paper presents a novel approach to dependency discovery within software systems, leveraging topological information derived from program code. The core idea is to automatically uncover complex dependencies between programs by analyzing control flow graphs, data dependencies, and abstract syntax trees (ASTs). A graph neural network (GNN) algorithm is developed to learn program representations and infer dependencies using graph convolutional operations. Furthermore, execution data is utilized to validate and refine the dependency graph. This method overcomes limitations of existing static analysis and monitoring-based tools by effectively utilizing program abstract representations, enabling the discovery of intricate dependencies often missed by traditional approaches. The research contributes to a more complete understanding of software ecosystems and facilitates improved dependency management, version control, and software evolution.