里约CC:基于深度代码克隆检测的高效准确的类级代码建议
Hongcan Gao1, Chenkai Guo2, Hui Yang3
1School of Information Engineering, Tianjin University of Commerce, Tianjin 300133, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
通过使用基于深森林的克隆检测来有效地缩小搜索空间,RioCC增强了类级代码推. 该框架在大型代码推任务中提高了编程效率和软件质量.
科学领域:
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 目前的代码推方法仅限于局部环境 (方法/API级).
- 需要类级代码推来处理大代码空间并保存结构信息.
- 现有的方法缺乏大规模代码推的效率和可扩展性.
研究的目的:
- 提出RioCC,一个新的类级代码推框架.
- 为了利用基于深森林的代码克隆检测来有效地减少候选空间.
- 在大型代码环境中提高推的效率和准确性.
主要方法:
- 里奥CC采用了从粗到细的候选物减少策略.
- 一个基于快速搜索的过模块执行初始候选人选.
- 基于深层森林的分析与级联学习和多粒度扫描完善了相似性评估.
主要成果:
- 在一个大数据集 (192,000个克隆对) 上,RioCC的性能优于最先进的方法 (CCLearner,Oreo,RSharer).
- 该框架显著加快了推过程,同时保持了可比的检测准确性.
- 在四种类型的代码克隆中,RioCC展示了卓越的性能.
结论:
- 类级代码推可以有效地建模为分阶段检索和改进问题.
- 里奥CC为大规模的代码推提供了一个高效和可扩展的解决方案.
- 将轻量级过与以森林为基础的深度学习相结合实际上是有价值的.
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