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ICSE 2018

CCAligner: a token based large-gap clone detector

Pengcheng Wang, Jeffrey Svajlenko, Yanzhao Wu, Yun Xu, Chanchal K. Roy

PDFDOI 10.1145/3180155.3180179dblp

Abstract

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.

BibTeX
@inproceedings{Wang-al:ICSE18,
  author    = {Pengcheng Wang and
               Jeffrey Svajlenko and
               Yanzhao Wu and
               Yun Xu and
               Chanchal K. Roy},
  title     = {{CCAligner:} a token based large-gap clone detector},
  booktitle = {ICSE},
  pages     = {1066--1077},
  publisher = {{ACM}},
  year      = {2018},
}

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