一个用于人类编写和人工智能生成代码源代码分类的数据集
Ghizlane Boukili1, Said El Garouani1, Jamal Riffi1
1LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez, 30003, Morocco.
Data in brief
|February 18, 2026
概括
一个由10,000个代码样本组成的新数据集有助于检测人工智能生成的代码. 这个资源有助于计算机科学教育工作者区分人类和人工智能编程,提高学术完整性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能代码生成工具在计算机科学教育中对验证学生真实性提出了挑战.
- 由于编程语言的特殊性,现有的通用AI检测工具不足以准确识别AI生成的代码.
研究的目的:
- 引入专门的数据集,用于开发特定领域的AI代码检测工具.
- 解决研究人工智能生成代码检测的资源缺口.
主要方法:
- 创建一个数据集,包含1万个注释代码样本 (5000个由人类编写,5000个由人工智能生成).
- 包含跨 Python,Java,C 和 C++ 的样本.
- 通过ChatGPT API生成的人工智能生成的样本;来自公共存储库的人类样本.
- 每个样本按原产地 (人类或人工智能) 标记,用于模型训练.
主要成果:
- 该数据集能够对机器学习和深度学习模型进行强有力的训练,以进行代码源区别.
- 促进开发专门的工具来检测人工智能生成的代码.
结论:
- 该专业数据集对于推进人工智能生成代码检测研究至关重要.
- 数据集和实验代码的公开可用性支持进一步的学术研究和工具开发.
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