CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generation

• URL: http://arxiv.org/abs/2504.13472v1
• Date: Fri, 18 Apr 2025 05:26:32 GMT
• Title: CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generation
• Authors: Xinchen Wang, Pengfei Gao, Chao Peng, Ruida Hu, Cuiyun Gao,
• Abstract summary: Large language models (LLMs) have demonstrated strong capabilities in code generation.<n>Existing evaluation approaches fall into three categories, including human-centered, metric-based, and LLM-based.<n>We propose CodeVisionary, the first LLM-based agent framework for evaluating LLMs in code generation.
• Score: 8.795746370609855
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) have demonstrated strong capabilities in code generation, underscoring the critical need for rigorous and comprehensive evaluation. Existing evaluation approaches fall into three categories, including human-centered, metric-based, and LLM-based. Considering that human-centered approaches are labour-intensive and metric-based ones overly rely on reference answers, LLM-based approaches are gaining increasing attention due to their stronger contextual understanding capabilities and superior efficiency. However, the performance of LLM-based approaches remains limited due to: (1) lack of multisource domain knowledge, and (2) insufficient comprehension of complex code. To mitigate the limitations, we propose CodeVisionary, the first LLM-based agent framework for evaluating LLMs in code generation. CodeVisionary consists of two stages: (1) Multiscore knowledge analysis stage, which aims to gather multisource and comprehensive domain knowledge by formulating and executing a stepwise evaluation plan. (2) Negotiation-based scoring stage, which involves multiple judges engaging in discussions to better comprehend the complex code and reach a consensus on the evaluation score. Extensive experiments demonstrate that CodeVisionary achieves the best performance for evaluating LLMs in code generation, outperforming the best baseline methods with average improvements of 0.202, 0.139, and 0.117 in Pearson, Spearman, and Kendall-Tau coefficients, respectively. Besides, CodeVisionary provides detailed evaluation reports, which assist developers in identifying shortcomings and making improvements. The resources of CodeVisionary are available at https://anonymous.4open.science/r/CodeVisionary.

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