A state of art on source code plagiarism detection

Agrawal, Mayank; Sharma, Dilip Kumar

doi:10.1109/ngct.2016.7877421

Cited by 20 publications

(7 citation statements)

References 6 publications

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“…Most detection techniques assume that all inputted source code files are written in the same programming language [2,21]. This can be an issue when more than one programming language is allowed to complete a particular assessment.…”

Section: Related Workmentioning

confidence: 99%

“…In some computer science courses, students are free to choose their preferred programming language for completing assignments. This can lead to an issue when plagiarism or collusion occurs; most existing detection techniques are not specifically designed to handle such cases in a cross-language manner where the involved source code files are written in different programming languages [2,21]. Some detection techniques have addressed the issue by considering the source code as raw text [7,50], removing any needs for language-specific components.…”

Section: Introductionmentioning

confidence: 99%

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TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Karnalim

2020

csci

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Several computing courses allow students to choose which programming language they want to use for completing a programming task. This can lead to cross-language code plagiarism and collusion, in which the copied code file is rewritten in another programming language. In response to that, this paper proposes a detection technique which is able to accurately compare code files written in various programming languages, but with limited effort in accommodating such languages at development stage. The only language-dependent feature used in the technique is source code tokeniser and no code conversion is applied. The impact of coincidental similarity is reduced by applying a TF-IDF inspired weighting, in which rare matches are prioritised. Our evaluation shows that the technique outperforms common techniques in academia for handling language conversion disguises. Further, it is comparable to those techniques when dealing with conventional disguises.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Karnalim

2020

csci

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“…Text Source Code Other Meuschke and Gipp [160] YES NO NO YES NO Chong [47] YES NO NO NO NO Eisa et al [61] YES NO YES NO NO Agarwal and Sharma [8] YES YES NO YES NO Chowdhury et al [48] YES YES NO YES NO Kanjirangat and Gupta [251] YES YES NO YES NO Velasquez et al [256] YES NO NO YES YES Hourrane and Benlahmar [114] YES NO NO NO NO…”

Section: Plagiarism Detection Tasks Systems Policies Papermentioning

confidence: 99%

“…Agarwal and Sharma [8] focused on source code PD but also gave a basic overview of plagiarism detection methods for text documents. Technologically, source code PD and PD for text are closely related, and many plagiarism detection methods for text can also be applied for source code PD [57].…”

Section: Plagiarism Detection Tasks Systems Policies Papermentioning

confidence: 99%

Untitled

2019

CSUR

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This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Specialized information retrieval; • Computing methodologies → Natural language processing; Machine learning approaches; • Applied computing → Digital libraries and archives;

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“…This is inevitable since they are developed to solve a common programming problem and share common programming language keywords. Plagiarism detection is a reactive mechanism, which highlights similarities between various code snippets well after the commitment of dishonest actions (Agrawal & Sharma, 2016;Joy & Luck, 1999).…”

Section: Literature Reviewmentioning

confidence: 99%

A Real-time Plagiarism Detection Tool for Computer-based Assessments

2018

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Aim/PurposeThe aim of this article is to develop a tool to detect plagiarism in real time amongst students being evaluated for learning in a computer-based assessment setting.Background Cheating or copying all or part of source code of a program is a serious concern to academic institutions. Many academic institutions apply a combination of policy driven and plagiarism detection approaches. These mechanisms are either proactive or reactive and focus on identifying, catching, and punishing those found to have cheated or plagiarized. To be more effective against plagiarism, mechanisms that detect cheating or colluding in real-time are desirable.

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A state of art on source code plagiarism detection

Cited by 20 publications

References 6 publications

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

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A Real-time Plagiarism Detection Tool for Computer-based Assessments

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