Dynamic Semantic Topology Learning and Program Reasoning

Abstract

This paper introduces a novel approach to program understanding and optimization, termed Dynamic Semantic Topology Learning and Program Reasoning. The core claim is that program behavior is not solely determined by its source code, but significantly influenced by the dynamic semantic relationship network within its runtime environment, which evolves during execution. We propose a mechanism utilizing Graph Neural Networks (GNNs) to construct and continuously update a program semantic topology. Nodes in this topology represent program elements such as functions, variables, and data structures, while edges represent their semantic dependencies – including call relationships, data flow relationships, and control flow relationships. A reinforcement learning-based GNN learning algorithm leverages feedback signals from program execution (e.g., results, errors, resource consumption) to adjust the topology, enhancing its accuracy in reflecting program behavior. This dynamic approach overcomes limitations of static semantic representations like Abstract Syntax Trees (ASTs) and Control Flow Graphs (CFGs) by capturing the complex, runtime-dependent relationships, ultimately leading to improved program understanding and optimization. The methodology offers a new paradigm for analyzing and reasoning about program logic, particularly in dynamic and complex systems.