Review of Code Similarity and Plagiarism Detection Research Studies

Author:

Lee Gunwoo1ORCID,Kim Jindae2ORCID,Choi Myung-seok1,Jang Rae-Young1ORCID,Lee Ryong1

Affiliation:

1. AI Data Research Center, Division of Science and Technology Digital Convergence, Korea Institute of Science and Technology Information (KISTI), Daejeon 34141, Republic of Korea

2. Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea

Abstract

The foundational technique of code similarity detection, which underpins plagiarism detection tools, has already reached a level of maturity where it can be effectively employed for practical applications, demonstrating commendable performance. However, although the understanding of code clones—referred to as similar codes—has evolved, there has been a noticeable decline in the emergence of novel proposals for code similarity detection techniques. The landscape of code similarity detection techniques is diverse and can be divided based on how codes are represented. Each method, designed to cater to different types of detectable code similarity instances, has distinct advantages and drawbacks. Therefore, the selection of an appropriate method is crucial and is contingent on the specific objectives of the analysis. This paper provides a comprehensive exploration of code similarity detection techniques and illuminates the prevailing trends in plagiarism detection research. It acquaints readers with a spectrum of distinct code similarity detection methods, accompanied by the requisite contextual background knowledge. Additionally, it presents a detailed overview of the trajectory of research trends in plagiarism detection.

Funder

Ministry of Science and ICT

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference74 articles.

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