ES-Plag: Efficient and sensitive source code plagiarism detection tool for academic environment

摘要

Source code plagiarism detection using Running-Karp-Rabin Greedy-String-Tiling (RKRGST) is a common practice in academic environment. However, such approach is time-inefficient (due to RKRGST's cubic time complexity) and insensitive (toward token subsequence rearrangement). This paper proposes ES-Plag, a plagiarism detection tool featured with cosine-based filtering and penalty mechanism to handle aforementioned issues. Cosine-based filtering mitigates time-inefficiency by excluding non-potential pairs from RKRGST comparison; while penalty mechanism mitigates insensitivity by reducing the number of matched tokens with the number of matched subsequences prior similarity normalization. In addition to issue-solving features, ES-Plag is also featured with project-based input, colorized adjacency similarity matrix, matched token highlighting, and various similarity algorithms (e.g., Cosine Similarity and Local Alignment). Three findings can be deducted from our evaluation. First, cosine-based filtering boosts up time efficiency with a trade-off in effectiveness. Second, penalty mechanism enhances sensitivity even though its improvement in terms of effectiveness is quite limited. Third, ES-Plag's features are beneficial for examiners.