一句话：提出 TreeCen，通过构建树图实现可扩展的语义代码克隆检测，以兼顾语义与规模。
代码克隆检测是软件工程中的重要问题。已有方法中，基于文本和 token 的方法可扩展但忽略代码语义，因而无法检测语义克隆；基于中间表示的方法可处理语义克隆，但图方法受代码编译限制，现有树方法又受树规模限制，难以支持大规模检测。为此，作者提出 TreeCen，通过构建树图（tree graph）来支持可扩展的语义代码克隆检测。标题与摘要表明其核心思路是以树图结构连接语义信息与可扩展性；但摘要未给出具体技术细节和实验结果，因此无法进一步说明其精度或性能提升。
Code clone detection is an important research problem that has attracted wide attention in software engineering. Many methods have been proposed for detecting code clone, among which text-based and token-based approaches are scalable but lack consideration of code semantics, thus resulting in the inability to detect semantic code clones. Methods based on intermediate representations of codes can solve the problem of semantic code clone detection. However, graph-based methods are not practicable due to code compilation, and existing tree-based approaches are limited by the scale of trees for scalable code clone detection.
@inproceedings{paperbot7246,
title = {TreeCen: Building Tree Graph for Scalable Semantic Code Clone Detection},
author = {Yutao Hu and Deqing Zou and Junru Peng and Yueming Wu and Junjie Shan and Hai Jin},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3556927}
}