使用混合中间表示和BiLSTM网络的语义代码克隆检测
M Shahbaz Ismail1, Sara Shahzad1, Fahmi H Quradaa2
1Department of Computer Science, University of Peshawar, Peshawar, Pakistan.
PloS one
|January 20, 2026
概括
本研究引入了一种用于使用混合表示和深度双向LSTM网络检测语义代码克隆的新框架. 该方法实现了高精度,优于其他模型,并在软件维护中证明了其实际适用性.
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
背景情况:
- 语义代码克隆检测对于软件维护和质量保证至关重要.
- 现有的方法与模糊代码扎,包括多态和变形.
研究的目的:
- 为语义代码克隆检测提出一个强大的框架.
- 为了应对代码模糊化技术带来的挑战.
主要方法:
- 使用混合表示学习与深度双向长短期记忆 (BiLSTM) 网络.
- 将模型应用于通过Soot框架提取的Baf和Jimple中间表示.
- 整合注意力机制和多个中间表示.
主要成果:
- 取得了高水平的培训 (98%) 和验证 (95%) 准确度,具有良好的概括性.
- BiLSTM 模型的表现始终优于其他经常性模型.
- 在BigCloneBench数据集上表现出最先进的性能,回忆和F1得分高达97%.
结论:
- 混合中间表示对于语义克隆检测是有效的.
- 拟议的BiLSTM框架为识别代码克隆提供了一个实际的解决方案,即使是在模糊的情况下.
- 未来的研究可以探索基于变压器的模型,以提高性能.
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