AI Chain-Driven Control Flow Graph Generation for Multiple Programming Language
Zhou Zou, Zhengkang Zuo, Qing Yun Huang · Wuhan University Journal of Natural Sciences · 2025
Control Flow Graphs (CFGs) are essential for understanding the execution and data flow within software, serving as foundational structures in program analysis. Traditional CFG construction methods, such as bytecode analysis and Abstract Syntax Trees (ASTs), often face challenges due to the complex syntax of programming languages like Java and Python. This paper introduces a novel approach that leverages Large Language Models (LLMs) to generate CFGs through a methodical Chain of Thought (CoT) process. By employing CoT, the proposed approach systematically interprets code semantics directly from natural language, enhancing the adaptability across various programming languages and simplifying the CFG construction process. By implementing a modular AI chain strategy that adheres to the single responsibility principle, our approach breaks down CFG generation into distinct, manageable steps handled by separate AI and non-AI units, which can significantly improve the precision and coverage of CFG nodes and edges. The experiments with 245 Java and 281 Python code snippets from Stack Overflow demonstrate that our method achieves efficient performance on different programming languages and exhibits strong robustness.