一句话：针对语义代码克隆检测中图匹配可扩展性低的问题，提出工具 SCDetector。
代码克隆检测用于发现功能相似的代码片段，在软件工程中日益重要。基于 token 的方法最具可扩展性，但未考虑程序语义，难以处理语义克隆。为处理语义克隆，研究者通过程序分析将程序语义提炼为图表示，并以图匹配检测克隆，然而图匹配通常耗时，导致这类方法可扩展性较低。SCDetector 针对上述矛盾提出。摘要未给出其具体技术路线、实现和实验结果，因此其方法与有效性需参见论文正文。
Code clone detection is to find out code fragments with similar functionalities, which has been more and more important in software engineering. Many approaches have been proposed to detect code clones, in which token-based methods are the most scalable but cannot handle semantic clones because of the lack of consideration of program semantics. To address the issue, researchers conduct program analysis to distill the program semantics into a graph representation and detect clones by matching the graphs. However, such approaches suffer from low scalability since graph matching is typically time-consuming.
DOI 原文 ·
@inproceedings{paperbot5985,
title = {SCDetector},
author = {Yueming Wu and Deqing Zou and Shihan Dou and Siru Yang and Wei Yang and Feng Cheng and Hong Liang and Hai Jin},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3416562}
}