Graph Neural Networks-Guided Code Optimization System for Software Performance Enhancement

The rapid nature of software system development necessitates new approaches to guarantee that there is optimization of code to achieve improved performance. The paper presents a Graph Neural Networks (GNN)-based Code Optimization System, which takes the form of graphs and represents the programs to identify performance bottlenecks and offer performance optimization guidelines. The system is founded on the applications of Graph Convolutional Network (GCNs) as its core method and realized with the assistance of the PyTorch Geometric system that can be discussed as effective in the capture of syntactic and semantic dependencies within the code. Experimental results of running benchmark software projects have revealed that there have been tremendous gains in the execution time, use of memory and computation efficiency compared to the conventional code optimization tools. The developed solution offers the software developers with a scaled and automated solution, reducing human intervention and reducing the high-performance software development cycles.