Dataflow Analysis

Dataflow analysis examines how data moves and transforms within a system, aiming to optimize performance, improve code understanding, and detect errors. Current research focuses on applying machine learning, particularly graph neural networks (like Graph Transformer Networks) and large language models, to automate dataflow analysis tasks, such as type inference and anomaly detection in dynamic systems. These advancements are improving the efficiency and accuracy of code analysis, impacting software development, cybersecurity, and the optimization of computationally intensive applications like neural network inference.