Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

Karnalim, Oscar and Budi, Setia and Toba, Hapnes and Joy, Mike (2019) Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation. Informatics in Education, 18 (2). pp. 321-344. ISSN 1648-5831

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Abstract

Source code plagiarism is an emerging issue in computer science education. As a result, a number of techniques have been proposed to handle this issue. However, comparing these tech�niques may be challenging, since they are evaluated with their own private dataset(s). This paper contributes in providing a public dataset for comparing these techniques. Specifically, the dataset is designed for evaluation with an Information Retrieval (IR) perspective. The dataset consists of 467 source code files, covering seven introductory programming assessment tasks. Unique to this dataset, both intention to plagiarise and advanced plagiarism attacks are considered in its construction. The dataset’s characteristics were observed by comparing three IR-based detection techniques, and it is clear that most IR-based techniques are less effective than a baseline tech�nique which relies on Running-Karp-Rabin Greedy-String-Tiling, even though some of them are far more time-efficient.

Item Type:	Article
Contributors:	Contribution Contributors NIDN/NIDK Email UNSPECIFIED Karnalim, Oscar UNSPECIFIED UNSPECIFIED UNSPECIFIED Budi, Setia UNSPECIFIED UNSPECIFIED UNSPECIFIED Toba, Hapnes UNSPECIFIED UNSPECIFIED UNSPECIFIED Joy, Mike UNSPECIFIED UNSPECIFIED
Uncontrolled Keywords:	source code plagiarism, dataset, programming, computer science education.
Subjects:	T Technology > T Technology (General)
Depositing User:	Perpustakaan Maranatha
Date Deposited:	30 Aug 2023 08:50
Last Modified:	30 Aug 2023 08:50
URI:	http://repository.maranatha.edu/id/eprint/32090

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