文章基本信息

标题：WASTK: A Weighted Abstract Syntax Tree Kernel Method for Source Code Plagiarism Detection
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作者：Deqiang Fu ; Yanyan Xu ; Haoran Yu 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2017
卷号：2017
DOI：10.1155/2017/7809047
出版社：Hindawi Publishing Corporation
摘要：In this paper, we introduce a source code plagiarism detection method, named WASTK (Weighted Abstract Syntax Tree Kernel), for computer science education. Different from other plagiarism detection methods, WASTK takes some aspects other than the similarity between programs into account. WASTK firstly transfers the source code of a program to an abstract syntax tree and then gets the similarity by calculating the tree kernel of two abstract syntax trees. To avoid misjudgment caused by trivial code snippets or frameworks given by instructors, an idea similar to TF-IDF (Term Frequency-Inverse Document Frequency) in the field of information retrieval is applied. Each node in an abstract syntax tree is assigned a weight by TF-IDF. WASTK is evaluated on different datasets and, as a result, performs much better than other popular methods like Sim and JPlag.