Towards Automating Precision Studies of Clone Detectors
Abstract
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 semi-automated 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.
Cite
@article{arxiv.1812.05195,
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 Lopes},
journal= {arXiv preprint arXiv:1812.05195},
year = {2019}
}
Comments
Accepted to be published in the 41st ACM/IEEE International Conference on Software Engineering