一句话：提出基于学习的电子表格表格克隆检测，以识别发生结构变化的克隆，弥补现有方法仅支持同结构克隆的不足。
电子表格中，终端用户常通过复制并修改已有表格来创建新表格，两者共享相似计算语义，构成表格克隆。用户对克隆中的表格进行增删行列等修改，会引入结构变化。作者对真实电子表格的实证研究显示，约58.5%的表格克隆涉及结构变化。然而，现有表格克隆检测方法只能识别结构完全相同的克隆，导致大量发生结构变化的克隆无法被检测。为此，论文提出基于学习的检测方法（标题所示），以期在这种结构变化场景下更好地检测表格克隆。摘要未展开具体模型、特征与实验评估细节。
In order to speed up spreadsheet development productivity, end users can create a spreadsheet table by copying and modifying an existing one. These two tables share the similar computational semantics, and form a table clone. End users may modify the tables in a table clone, e.g., adding new rows and deleting columns, thus introducing structure changes into the table clone. Our empirical study on real-world spreadsheets shows that about 58.5% of table clones involve structure changes. However, existing table clone detection approaches in spreadsheets can only detect table clones with the same structures. Therefore, many table clones with structure changes cannot be detected.
DOI 原文 ·
@inproceedings{paperbot6053,
title = {Learning to detect table clones in spreadsheets},
author = {Yakun Zhang and Wensheng Dou and Jiaxin Zhu and Liang Xu and Zhiyong Zhou and Jun Wei and Dan Ye and Bo Yang},
booktitle = {ISSTA},
year = {2020},
doi = {10.1145/3395363.3397384}
}