Source Code Plagiarism Detection Using Biological String Similarity Algorithms

Abstract

Source code plagiarism is easy to commit but di±cult to catch. Many approaches have been proposed in the literature to automate its detection; however there is little consensus on what works best. In this paper, we propose two new measures for determining the accuracy of a given technique and describe an approach to convert code¯les into strings which can then be compared for similarity in order to detect plagiarism. We then compare several string comparison techniques, heavily utilised in the area of biological sequence alignment, and compare their performance on a large collection of student source code containing various types of plagiarism. Experimental results show that the compared techniques succeed in matching a pla-giarised¯le to its original¯les upwards of 90% of the time. Finally, we propose a modi¯cation for these algorithms that drastically improves their runtimes with little or no e®ect on accuracy. Even though the ideas presented herein are applicable to most programming languages, we focus on a case study pertaining to an introductory-level Visual Basic programming course o®ered at our institution.

Detecting Source Code Plagiarism in Student Assignment Submissions Using Clustering Techniques

“…Since it is not provided with labels, the learning system must discover how the information is organized on its own. Clustering algorithms are also a part of this group of algorithms [3].…”

“…Plagiarism, on the other hand, is defined as the copying of written materials and computer code. Source code plagiarism is defined as attempting to pass off another person's source code as one's own while omitting to recognize which precise sections were copied from which author [3]. Plagiarism in source code is common in academic programming coursework [4].…”

On the Detection of SOurce COde Re-use

“…Such methods, as stated in [21], involve more complex as well as robust approaches. Normally, source code files are treated as text files, hence, common methods such as the traditional Bagof-Words, character n-grams [12,22], and longest common sub-sequence [2,15] are among the most popular techniques.…”

Dolos: Language‐agnostic plagiarism detection in source code

“…This practice is defined as source code plagiarism by Cosma and Joy (2008). It involves obtaining source code either with or without the permission of the original author, and submitting the copied code with no, minor, or even major modifications aimed at concealing plagiarism (Rahal & Wielga, 2014).…”