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        <title>CCAligner</title>
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    <name type="personal">
        <namePart type="given">Pengcheng</namePart>
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    <name type="personal">
        <namePart type="given">Jeffrey</namePart>
        <namePart type="family">Svajlenko</namePart>
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    <name type="personal">
        <namePart type="given">Yanzhao</namePart>
        <namePart type="family">Wu</namePart>
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    <name type="personal">
        <namePart type="given">Yun</namePart>
        <namePart type="family">Xu</namePart>
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    <name type="personal">
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    <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.</abstract>
    <identifier type="citekey">Wang-2018-CCAligner</identifier>
    <identifier type="doi">10.1145/3180155.3180179</identifier>
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        <url>https://gwf-uwaterloo.github.io/gwf-publications/G18-6001</url>
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        <date>2018</date>
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%0 Journal Article
%T CCAligner
%A Wang, Pengcheng
%A Svajlenko, Jeffrey
%A Wu, Yanzhao
%A Xu, Yun
%A Roy, Chanchal K.
%J Proceedings of the 40th International Conference on Software Engineering
%D 2018
%I ACM
%F Wang-2018-CCAligner
%X 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.
%R 10.1145/3180155.3180179
%U https://gwf-uwaterloo.github.io/gwf-publications/G18-6001
%U https://doi.org/10.1145/3180155.3180179

Markdown (Informal)

[CCAligner](https://gwf-uwaterloo.github.io/gwf-publications/G18-6001) (Wang et al., GWF 2018)

• CCAligner (Wang et al., GWF 2018)

ACL

• Pengcheng Wang, Jeffrey Svajlenko, Yanzhao Wu, Yun Xu, and Chanchal K. Roy. 2018. CCAligner. Proceedings of the 40th International Conference on Software Engineering.