Graph Neural Networks for Program Abstraction and Optimization

Abstract

This paper explores the application of Graph Neural Networks (GNNs) to the problem of program abstraction and optimization. Traditional approaches to program abstraction often rely on manually crafted rules or limited pattern matching, which can be brittle and difficult to scale. This work proposes a novel framework that leverages the power of GNNs to learn a high-level, abstract representation of program code from its underlying graph structure. The learned representations are then utilized to perform optimizations such as variable renaming, code simplification, and potentially, the generation of equivalent, more efficient code. We demonstrate the feasibility and potential benefits of this approach, highlighting its ability to capture complex relationships within code and offer a more robust and scalable solution compared to rule-based methods. The core claim is to utilize GNNs to learn program code's abstract representation and optimize the code for improved efficiency and maintainability. The core mechanism involves representing code as a graph structure and applying GNNs for both abstraction and optimization. This approach overcomes the limitations of traditional methods by incorporating deep learning capabilities for a more nuanced understanding of program semantics. ---