Fine-grained Block Clone Detection Based on Information Retrieval Techniques.

DOI HANDLE Web Site Open Access
  • YOKOI Kazuki
    Graduate School of Information Science and Technology, Osaka University
  • CHOI Eunjong
    Graduate School of Science and Technology, Nara Institute of Science and Technology
  • YOSHIDA Norihiro
    Graduate School of Informatics, Nagoya University
  • INOUE Katsuro
    Graduate School of Information Science and Technology, Osaka University

Bibliographic Information

Other Title
  • 情報検索技術に基づく細粒度ブロッククローン検出
  • ジョウホウ　ケンサク　ギジュツ　ニ　モトヅク　サイリュウド　ブロック　クローン　ケンシュツ
  • ジョウホウ ケンサク ギジュツ ニ モトズク サイリュウド ブロッククローン ケンシュツ

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Abstract

<p>In this paper, we propose an approach for detecting block clones (i.e., block-level code clones) using information retrieval techniques. In the previous study, we have presented an approach for detecting function clones. However, the previous approach misses a number of code clones because it only identifies coarse-grained code clones (i.e., function clones). To mitigate this problem, this study proposes an approach that detects block clones by generating feature vectors for code blocks based on the occurrence of identifiers and reserved keywords and then performing clustering of the generated vectors. Also, we improve a clustering method and a data structure of feature vectors in the proposed approach. It leads not only the detection of fine-grained code clones but also less detection time and low memory consumption, compared to the previous approach.As a case study, we compared the proposed approach with the existing code clone approaches and then confirmed the effectiveness of the proposed approach.</p>

Journal

  • Computer Software

    Computer Software 35 (4), 16-36, 2018-10-25

    Japan Society for Software Science and Technology

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