SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors
arXiv:2502.11167v5 Announce Type: replace-cross 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 comprehensive benchmark with $1160$ problems covering $8$ key aspects: multi-language programming tasks, competition-level programming p
The increasing sophistication of large language models in code-related tasks makes exploring their potential as surrogate code executors a natural next step in AI research.
This research suggests that LLMs could automate and optimize complex code execution tasks, impacting software development, testing, and system design workflows significantly.
The ability of LLMs to act as predictive surrogate models for code execution could accelerate software iteration cycles and reduce reliance on actual execution environments for certain tasks.
- · AI research and development teams
- · Software development companies
- · Cloud computing providers
- · DevOps and MLOps platforms
- · Traditional code testing and debugging tool vendors (if they don't adapt)
- · Manual code reviewers (in certain contexts)
- · Firms reliant on inefficient code execution pipelines
LLMs demonstrate enhanced capabilities in predicting code behavior without explicit execution.
This leads to faster development cycles and more efficient testing methodologies for complex software systems.
The abstraction of code execution by LLMs could enable entirely new paradigms for software creation and maintenance, potentially allowing non-programmers to 'simulate' code execution through natural language interfaces.
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Read at arXiv cs.CL