document: GPLAG : detection of software plagiarism by program dependence graph analysis Along with the blossom of open source projects comes the convenience for software plagiarism . A company , if less self-disciplined , may be tempted to plagiarize some open source projects for its own products . Although current plagiarism detection tools appear sufficient for academic use , they are nevertheless short for fighting against serious plagiarists . For example , disguises like statement reordering and code insertion can effectively confuse these tools . In this paper , we develop a new plagiarism detection tool , called GPLAG , which detects plagiarism by mining program dependence graphs ( PDGs ) . A PDG is a graphic representation of the data and control dependencies within a procedure . Because PDGs are nearly invariant during plagiarism , GPLAG is more effective than state-of-the-art tools for plagiarism detection . In order to make GPLAG scalable to large programs , a statistical lossy filter is proposed to prune the plagiarism search space . Experiment study shows that GPLAG is both effective and efficient : It detects plagiarism that easily slips over existing tools , and it usually takes a few seconds to find ( simulated ) plagiarism in programs having thousands of lines of code .
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