一句话：提出半自动化克隆检测器精度评估方法，用自动分类减少人工验证并汇聚标注数据集。
克隆检测研究长期缺乏良好的精度评估生态：带标注的克隆语料稀缺且不完整，导致评估费时、主观性强，也难以进行工具间比较，且几乎没有专门的精度评估工具。本文提出一种半自动化方法，将自动克隆分类与人工验证克隆对相结合，用于支持克隆检测工具的精度研究。实验表明，该自动方法具有很高精度，能显著减少精度实验中需要人工验证的克隆对数量。作者还把多个团队的人工标注结果汇总为持续演化的标注克隆对数据集，为软件克隆研究提供重要资产。
Current research in clone detection suffers from poor ecosystems for evaluating precision of clone detection tools. Corpora of labeled clones are scarce and incomplete, making evaluation labor intensive and idiosyncratic, and limiting intertool comparison. Precision-assessment tools are simply lacking. We present a semiautomated approach to facilitate precision studies of clone detection tools. The approach merges automatic mechanisms of clone classification with manual validation of clone pairs. We demonstrate that the proposed automatic approach has a very high precision and it significantly reduces the number of clone pairs that need human validation during precision experiments. Moreover, we aggregate the individual effort of multiple teams into a single evolving dataset of labeled clone pairs, creating an important asset for software clone research.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{SainiFLY0SBL19,
title = {Towards Automating Precision Studies of Clone Detectors},
author = {Vaibhav Saini and Farima Farmahinifarahani and Yadong Lu and Di Yang and Pedro Martins and Hitesh Sajnani and Pierre Baldi and Cristina V. Lopes},
booktitle = {ICSE},
year = {2019},
doi = {10.1109/icse.2019.00023}
}