2013 · 65 citations · 10 references
ComputingComputer ScienceData MiningInformation RetrievalSoftware EngineeringAbstract Syntax TreesData-flow AnalysisPlagiarism DetectionControl Flow Graph AnalysisImportance MeasuresImportance ScoringMachine Learning Approaches To Plagiarism DetectionSource Code Plagiarism Detection
As plagiarism of software increases rapidly, there are growing needs for software plagiarism detection systems. In this paper, we propose a software plagiarism detection system using an API-labeled control flow graph (A-CFG) that abstracts the functionalities of a program. The A-CFG can reflect both the sequence and the frequency of APIs, while previous work rarely considers both of them together. To perform a scalable comparison of a pair of A-CFGs, we use random walk with restart (RWR) that computes an importance score for each node in a graph. By the RWR, we can generate a single score vector for an A-CFG and can also compare A-CFGs by comparing their score vectors. Extensive evaluations on a set of Windows applications demonstrate the effectiveness and the scalability of our proposed system compared with existing methods.
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