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Efficient plagiarism detection for large code repositories

机译:大型代码存储库的有效窃检测

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摘要

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.
机译:学生在未经授权的情况下重用代码是学术机构中普遍存在的问题,并引起了业界的责任问题。手动窃检测非常耗时,并且当前有效的窃检测方法无法轻松扩展到非常大的代码存储库。尽管有一些实用的基于文本的窃检测系统可以处理大量馆藏,但基于代码的窃检测却并非如此。在本文中,我们提出了使用文本相似性度量和局部对齐来检测程序代码中抄袭的技术。通过对大型和小型程序集进行详细的经验评估,我们表明,我们的方法具有很高的可扩展性,同时保持了与流行的JPlag和MOSS系统相似的有效性。

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