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Source Code Plagiarism Detection Using Biological String Similarity Algorithms

Author

Listed:
  • Imad Rahal

    (Department of Computer Science, College of Saint Benedict and Saint John's University, Collegeville, MN 56321, USA)

  • Colin Wielga

    (Natural Sciences Division, New College of Florida, Sarasota, FL 34234, USA)

Abstract

Source code plagiarism is easy to commit but difficult 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 files 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 plagiarised file to its original files upwards of 90% of the time. Finally, we propose a modification for these algorithms that drastically improves their runtimes with little or no effect 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 offered at our institution.

Suggested Citation

  • Imad Rahal & Colin Wielga, 2014. "Source Code Plagiarism Detection Using Biological String Similarity Algorithms," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-22.
    • Handle: RePEc:wsi:jikmxx:v:13:y:2014:i:03:n:s0219649214500282
    DOI: 10.1142/S0219649214500282
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