Efficient plagiarism detection for large code repositories

  • Authors:

  • Steven Burrows;S. M. M. Tahaghoghi;Justin Zobel

  • Affiliations:

  • School of Computer Science and Information Technology, RMIT University, Melbourne, Australia;School of Computer Science and Information Technology, RMIT University, Melbourne, Australia;School of Computer Science and Information Technology, RMIT University, Melbourne, Australia

  • Venue:
  • Software—Practice & Experience
  • Year:
  • 2007

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Abstract

Unauthorized re-use of code by students is a widespread problem in academic institutions, and raises liability issues for industry. Manual plagiarism detection is time-consuming, and current effective plagiarism detection approaches cannot be easily scaled to very large code repositories. While there are practical text-based plagiarism detection systems capable of working with large collections, this is not the case for code-based plagiarism detection. In this paper, we propose techniques for detecting plagiarism in program code using text similarity measures and local alignment. Through detailed empirical evaluation on small and large collections of programs, we show that our approach is highly scalable while maintaining similar levels of effectiveness to that of the popular JPlag and MOSS systems. Copyright © 2006 John Wiley & Sons, Ltd.