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Code Execution with Pre-trained Language Models

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arxiv 2305.05383 v1 pith:CIQGGQI4 submitted 2023-05-08 cs.PL cs.AIcs.CLcs.SE

Chenxiao Liu , Shuai Lu , Weizhu Chen , Daxin Jiang , Alexey Svyatkovskiy , Shengyu Fu , Neel Sundaresan , Nan Duan This is my paper · ORCID
classification cs.PLcs.AIcs.CLcs.SE
keywords codeexecutionmodelspre-trainedcodeexecutorintelligencelanguagelearning
verification ladder T0 review T1 audit T2 compute T3 formal
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Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code. However, most pre-trained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures. In this paper, we investigate how well pre-trained models can understand and perform code execution. We develop a mutation-based data augmentation technique to create a large-scale and realistic Python dataset and task for code execution, which challenges existing models such as Codex. We then present CodeExecutor, a Transformer model that leverages code execution pre-training and curriculum learning to enhance its semantic comprehension. We evaluate CodeExecutor on code execution and show its promising performance and limitations. We also demonstrate its potential benefits for code intelligence tasks such as zero-shot code-to-code search and text-to-code generation. Our analysis provides insights into the learning and generalization abilities of pre-trained models for code execution.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hierarchical Domain Generalization

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Over infinite domains, hierarchy-uniform domain generalization is impossible for every nontrivial hypothesis class; a length-generalization bound is a property of the length hierarchy, not a hierarchy-free guarantee.

  2. CodeReasoner: Enhancing the Code Reasoning Ability with Reinforcement Learning

    cs.SE 2025-07 conditional novelty 6.0 of 10

    CodeReasoner combines a concise execution-focused dataset, instruction tuning, and GRPO RL to make 7B/14B models match or beat GPT-4o on code reasoning benchmarks.

  3. SV-TrustEval-C: Evaluating Structure and Semantic Reasoning in Large Language Models for Source Code Vulnerability Analysis

    cs.SE 2025-05 conditional novelty 6.0 of 10

    Large language models perform poorly on a new C-code vulnerability benchmark, indicating they rely on pattern matching rather than genuine reasoning.

  4. Code Simulation as a Proxy for High-order Tasks in Large Language Models

    cs.LG 2025-02 conditional novelty 5.0 of 10

    LLM performance on naturalistic reasoning tasks tracks performance on equivalent Python code simulation, but the effect is partly driven by pattern matching and memorization rather than faithful execution.

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