Towards Automating Precision Studies of Clone Detectors

被引:9
作者
Saini, Vaibhav [1 ]
Farmahinifarahani, Farima [1 ]
Lu, Yadong [1 ]
Yang, Di [1 ]
Martins, Pedro [1 ]
Sajnanit, Hitesh [2 ]
Baldi, Pierre [1 ]
Lopes, Cristina V. [1 ]
机构
[1] Univ Calif Irvine, Irvine, CA 92717 USA
[2] Microsoft, Redmond, WA USA
来源
2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2019) | 2019年
关键词
Precision Evaluation; Clone Detection; Machine learning; Open source labeled datasets;
D O I
10.1109/ICSE.2019.00023
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 inter-tool 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.
引用
收藏
页码:49 / 59
页数:11
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