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<p>Just like natural language, code can exhibit naturalness. This property manifests in highly repetitive patterns within specific contexts. Code naturalness can be captured by language models and then applied to various software engineering tasks (such as fault localization and program repair). Recently, Large Language Models (LLMs) based on Transformers have become advantageous tools for modeling code naturalness. However, existing work lacks systematic studies on the code naturalness modeling capability for LLMs. To bridge this gap, this paper explores the code naturalness modeling capability for LLMs, starting with the task of automated patch correctness assessment. Specifically, we investigate whether LLMs with different architectures and scales, under varying context window sizes, (1) can identify buggy code from common code based on naturalness and consider fixed code more natural than buggy code, and (2) can distinguish different degrees of repairs (i.e., complete repairs and incomplete repairs) from automated tools. Then, we propose metrics to assess the above two capabilities of the models. Experimental results indicate that models with different architectures and scales have the code naturalness modeling capability, even models not specifically pre-trained on code. Additionally, smaller models do not necessarily exhibit weaker modeling capability compared to larger models. We also find more contextual information only provides limited benefits. Based on experimental findings, we select the best performing model that has 220&#xa0;M parameters to develop an Entropy-based Automated Patch Correctness Assessment (E-APCA) approach by calculating code naturalness. On the large-scale dataset PraPatch, E-APCA surpasses traditional methods by over 20% across various evaluation metrics. Compared to the latest APCA method Entropy-delta based on a 6.7B LLM, E-APCA achieves a 17.32% higher correct patch recall and a 6.83% higher F1 score, while the reasoning time is less than 7% of that required by Entropy-delta.</p>

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An empirical study on the code naturalness modeling capability for LLMs in automated patch correctness assessment

  • 期刊论文

  • 发表日期：

    2025年4月2日

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  • Yuning Li,
  • Wenkang Zhong,
  • Zongwen Shen,
  • Chuanyi Li,
  • Xiang Chen,
  • Jidong Ge,
  • Bin Luo

摘要

Just like natural language, code can exhibit naturalness. This property manifests in highly repetitive patterns within specific contexts. Code naturalness can be captured by language models and then applied to various software engineering tasks (such as fault localization and program repair). Recently, Large Language Models (LLMs) based on Transformers have become advantageous tools for modeling code naturalness. However, existing work lacks systematic studies on the code naturalness modeling capability for LLMs. To bridge this gap, this paper explores the code naturalness modeling capability for LLMs, starting with the task of automated patch correctness assessment. Specifically, we investigate whether LLMs with different architectures and scales, under varying context window sizes, (1) can identify buggy code from common code based on naturalness and consider fixed code more natural than buggy code, and (2) can distinguish different degrees of repairs (i.e., complete repairs and incomplete repairs) from automated tools. Then, we propose metrics to assess the above two capabilities of the models. Experimental results indicate that models with different architectures and scales have the code naturalness modeling capability, even models not specifically pre-trained on code. Additionally, smaller models do not necessarily exhibit weaker modeling capability compared to larger models. We also find more contextual information only provides limited benefits. Based on experimental findings, we select the best performing model that has 220 M parameters to develop an Entropy-based Automated Patch Correctness Assessment (E-APCA) approach by calculating code naturalness. On the large-scale dataset PraPatch, E-APCA surpasses traditional methods by over 20% across various evaluation metrics. Compared to the latest APCA method Entropy-delta based on a 6.7B LLM, E-APCA achieves a 17.32% higher correct patch recall and a 6.83% higher F1 score, while the reasoning time is less than 7% of that required by Entropy-delta.