Plagiarism detection in student programs based on frequent closed sequence mining

Plagiarism in student programs is a common phenomenon, which decreases the credibility of assessment. However, manual detection loads a heavy burden on the teachers. To solve this problem, a plagiarism detection model is proposed. First, student programs are converted into token sequences through lexical analysis. Then, the token sequences are hashed to digital sequences. Then, the frequent closed sequences are mined by the BIDE algorithm. On this basis, the similar code fragments are detected and the plagiarism programs are identified by the calculated similarity. Experimental results show that, compared with the commonly used toll MOSS, the proposed method is more precise. It can not only give accurate statistical information of similar programs, but also explicitly display the plagiarized code fragments.