Enhancing the performance of code-clone detection tools using code2vec (code2vec을 이용한 유사도 감정 도구의 성능 개선)

Taeho Um (엄태호); Sung-Moon Hong (홍성문); Joon Hyuk Yang (양준혁); Hyo Seok Jang (장효석); Doh, Kyung-Goo (도경구)

doi:10.29056/jsav.2021.06.05

Enhancing the performance of code-clone detection tools using code2vec

Journal of Software Assessment and Valuation
Abbr : JSAV
2021, 17(1), pp.31-40
DOI : 10.29056/jsav.2021.06.05
Publisher : Korea Software Assessment and Valuation Society
Research Area : Engineering > Computer Science
Received : June 5, 2021
Accepted : June 20, 2021
Published : June 30, 2021

Taeho Um ¹, Sung-Moon Hong ¹, Joon Hyuk Yang ¹, Hyo Seok Jang ¹, Doh, Kyung-Goo ¹

¹한양대학교

Accredited

ABSTRACT

Plagiarism refers to the act of using the original data as if it were one’s own without revealing the source. The plagiarism of source code causes a variety of problems, including legal disputes. Plagiarism in software projects is usually determined by measuring similarity by comparing every pair of source code within two projects. However, blindly comparing every pair has been a huge computational burden, causing a major factor of not using tools of better accuracy. If we can only compare pairs that are probable to be clones, eliminating pairs that are impossible to be clones, we can concentrate more on improving the accuracy of detection. In this paper, we propose a method of selecting highly probable candidates of clone pairs by pre-classifying suspected source-codes using a machine-learning model called code2vec.

KEYWORDS

program similarity, program plagiarism, machine learning, code clone, code comparison

Citation status

* References for papers published after 2023 are currently being built.

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Journal of Software Assessment and Valuation 2023 KCI Impact Factor : 0.35

Enhancing the performance of code-clone detection tools using code2vec

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Citation status

* References for papers published after 2023 are currently being built.

Journal of Software Assessment and Valuation 2023 KCI Impact Factor : 0.35

Enhancing the performance of code-clone detection tools using code2vec

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KEYWORDS

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* References for papers published after 2023 are currently being built.