融合语义和结构特征用于代码错误检测和错误检测
Yiwen Zhang1, Wei Liu2, Fazhong Jiang3
1National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
大型语言模型 (LLM) 对代码错误检测有希望,但在结构依赖性方面存在困难. 结合RoBERTa和图形神经网络的新混合模型提高了常见编程错误的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 软件工程 软件工程 软件工程
背景情况:
- 基于变压器架构的大型语言模型 (LLM) 擅长处理顺序数据,显示自动代码错误检测的潜力.
- 然而,目前的LLM在有效处理结构代码依赖性方面存在局限性,这阻碍了它们在代码分析中的表现.
研究的目的:
- 引入一种新的混合模型,将RoBERTa的语义理解与图形神经网络 (GNN) 的结构学习能力相结合.
- 提高自动代码错误检测的准确性和稳定性,特别针对常见的编程错误,如运行时,索引和导入/模块错误.
主要方法:
- 开发一种混合模型,将罗伯塔用于语义分析和GNN用于结构依赖学习.
- 实施融合技术,有效地整合两个组件的输出.
- 对代码错误检测任务的混合模型与基线模型进行比较的实验评估.
主要成果:
- 拟议的混合模型在检测编程错误方面,与现有模型相比,显示出更高的性能.
- 实验评估显示,在准确性和稳定性方面有显著的改进.
- 该模型在测试准确度上比竞争对手的基线提高了1.75%.
结论:
- 通过混合的RoBERTa-GNN模型集成语义和结构学习,有效地解决了传统LLM在代码错误检测方面的局限性.
- 开发的融合技术对于模型的增强性能至关重要.
- 这种方法为识别常见的编程错误提供了更强大,更准确的解决方案,推进了自动化软件质量保证.
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