一句话：提出基于代码窗口与编辑距离的 CCAligner，高效检测大规模差距克隆并兼顾通用克隆检测。
软件开发中常通过复制代码并大量编辑来复用代码，所得代码属于复杂 Type-3 克隆，称为大规模差距克隆（large-gap clone）。现有克隆检测器在检测此类克隆时存在局限。本文提出工具 CCAligner，采用考虑编辑距离的代码窗口进行匹配，设计新的 e-mismatch 索引，并使用非对称相似系数度量相似度。作者在大规模差距克隆及一般 Type-1、Type-2、Type-3 克隆检测上评估 CCAligner。结果表明，CCAligner 在大规模差距克隆检测上优于其他竞争工具；在 10MLOC 输入上执行时间最佳，并在一般 Type-1 至 Type-3 克隆检测中保持良好精确率与召回率，整体与最佳检测器相当。
Copying code and then pasting with large number of edits is a common activity in software development, and the pasted code is a kind of complicated Type-3 clone. Due to large number of edits, we consider the clone as a large-gap clone. Large-gap clone can reflect the extension of code, such as change and improvement. The existing state-of-the-art clone detectors suffer from several limitations in detecting large-gap clones. In this paper, we propose a tool, CCAligner, using code window that considers e edit distance for matching to detect large-gap clones. In our approach, a novel e-mismatch index is designed and the asymmetric similarity coefficient is used for similarity measure. We thoroughly evaluate CCAligner both for large-gap clone detection, and for general Type-1, Type-2 and Type-3 clone detection. The results show that CCAligner performs better than other competing tools in large-gap clone detection, and has the best execution time for 10MLOC input with good precision and recall in general Type-1 to Type-3 clone detection. Compared with existing state-of-the-art tools, CCAligner is the best performing large-gap clone detection tool, and remains competitive with the best clone detectors in general Type-1, Type-2 and Type-3 clone detection.
DOI 原文 ·
@inproceedings{paperbot4492,
title = {CCAligner},
author = {Pengcheng Wang and Jeffrey Svajlenko and Yanzhao Wu and Yun Xu and Chanchal K. Roy},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3180179}
}