Demystify the Potential of Large Language Models as General-Purpose Surrogate Code Executors

Bohan Lyu, Siqiao Huang, Zichen Liang, Wenjia Yang, Qian Sun, Jiaming Zhang

Keywords: LLM for Code, Surrogate Model

TL;DR: We tested whether AI language models can predict code execution results without actually running the code, finding they show promise but still have significant limitations.

Abstract: Neural surrogate models are powerful and efficient tools in data mining. Meanwhile, large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks, such as generation and understanding. However, an equally important yet underexplored question is whether LLMs can serve as surrogate models for code execution prediction. To systematically investigate it, we introduce SURGE, a holistic benchmark with $1160$ problems covering $8$ key aspects: multi-language programming tasks, competition-level programming problems, repository-level code analysis, high-cost scientific computing, time-complexity-intensive algorithms, buggy code analysis, programs dependent on specific compilers or execution environments, and formal mathematical proof verification. Through extensive analysis of $21$ open-source and proprietary LLMs, we examine scaling laws, data efficiency, and predictive accuracy. Our findings reveal important insights about the feasibility of LLMs as efficient surrogates for computational processes.

Submission Number: 21