对于现实世界的专家编程人员来说,造型测量:一种零射击方法
Andrea Gurioli1, Maurizio Gabbrielli1, Stefano Zacchiroli2
1Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.
PeerJ. Computer science
|December 9, 2024
概括
这项研究引入了代码造型测量的新方法,即使它们不在培训数据中,也可以准确识别软件作者. 这有助于提升抄袭检测和代码审计技术.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 数字法医学数字法医学
背景情况:
- 代码造型测量识别软件作者使用风格特征.
- 现有的方法通常使用有限的,人工数据集,并与未知作者作斗争.
- 应用包括抄袭检测,代码审计和分配验证.
研究的目的:
- 通过使用现实世界的开源代码来挑战现有的代码样式测量假设.
- 开发一种识别培训数据中不存在的作者 (外发作者) 的方法.
- 创建和利用一个新的,大规模的数据集,用于代码造型测量.
主要方法:
- 汇集了来自104位作者的114,400个代码片段的新数据集.
- 在这个数据集上开发和训练了一个K-最近邻居 (k-NN) 分类器.
- 对在发行和在发行之外的作者进行评估的表现.
主要成果:
- 在发行版作者中达到69%的准确性,超过了20%以上的最先进状态.
- 保持了与外发行作者的高性能,达到71%的准确性.
- 证明了k-NN分类器在现实世界代码数据上的有效性.
结论:
- 开发的方法成功地从现实世界的开源项目中识别出作者.
- 该方法在识别初始培训组之外的作者方面非常强大.
- 这项工作为实际应用显著推进了代码造型测量.
关键词:
代码作者的归属 代码作者的归属这是一个代码片段.代码造型仪表的代码造型仪表数据挖掘是一种数据挖掘.深度学习是一种深度学习.机器学习 机器学习计量学学习的学习方法源代码 源代码 源代码零射击 零射击 零射击 零射击更多相关视频
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