Automated Detection Approaches for Source Code Plagiarism in Students' Submissions

Eliwa, Essam; Essam, Shereen; Ashraf, Mohamed; Sayed, Abdelrahman

doi:10.21608/jocc.2023.307054

Automated Detection Approaches for Source Code Plagiarism in Students' Submissions

Document Type : Original Article

Authors

¹ Software Engineering, Computer Science, MIU, Egypt

² Computer Science, Misr International University

³ Faculty of Computer Science, Misr International University, Cairo, Egypt

10.21608/jocc.2023.307054

Abstract

Code plagiarism is a significant concern in software development, as it compromises the integrity of original work and can lead to ethical and legal consequences. The need for effective plagiarism detection techniques has grown in parallel with the rise in online coding resources and collaborative platforms. The paper analyses existing plagiarism detection tools, comparing their characteristics, functions, and development timelines. Emphasis is placed on essential factors such as additional case detection, direct display of matched pairings, and compatibility with multiple programming languages. By examining these features, educators and software developers can decide which tools best suit their needs.
Additionally, the paper explores various plagiarism detection techniques, including attribute counting, content comparison, string tiling, and parse tree comparison. The advantages and limitations of each method are examined, underscoring the need for continuous improvement and innovation in the field. This paper presents the most widely available plagiarism detection tools that can be seamlessly integrated into learning management systems. In conclusion, the paper highlights critical areas for future research and development in plagiarism detection. These include the integration of plagiarism detection with live learning management systems to streamline the process for educators and students, the enhancement of usability and user experience in plagiarism detection tools to facilitate their adoption, the advancement of detection algorithms to improve accuracy, and the support for multi-language and cross-language comparisons to cater to diverse programming environments.

Keywords

References

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