CodeJudge: Evaluating Code Generation with Large Language Models

2024/10/03 by Tianyi Zhang, Tong, Weixi, Zhang, Tianyi · 9 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model-Driven Software Engineering Techniques #Natural Language Processing Techniques #Software Engineering (cs.SE) #Software Engineering Research

paper · pdf · doi:10.48550/arxiv.2410.02184

openalex publication_date 2024/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Large Language Models (LLMs) have shown promising performance in code generation. However, how to reliably evaluate code generated by LLMs remains an unresolved problem. This paper presents CodeJudge, a code evaluation framework that leverages LLMs to evaluate the semantic correctness of generated code without the need for test cases. We investigate different ways to guide the LLM in performing "slow thinking" to arrive at an in-depth and reliable evaluation. We experimented with four LLMs as evaluators on four code generation datasets and five programming languages. The results show that CodeJudge significantly outperformed existing methods in most settings. Furthermore, compared with a SOTA GPT-3.5-based code evaluation method, CodeJudge achieved better results even when using a much smaller model, Llama-3-8B-Instruct. Our code and datasets are available on GitHub https://github.com/VichyTong/CodeJudge.

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• Turning the Spell Around: Lightweight Alignment Amplification via Rank-One Safety Injection
• Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications
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Related

• CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?
• A Survey on Evaluating Large Language Models in Code Generation Tasks
• CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks
• Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review
• CodeArena: A Collective Evaluation Platform for LLM Code Generation