Efficient plagiarism detection for software modeling assignments
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Reports suggest plagiarism is a common occurrence in universities. While plagiarism detection mechanisms exist for textual artifacts, this is less so for non-code related ones such as software design artifacts like models, metamodels or model transformations.
To provide an efficient mechanism for the detection of plagiarism in repositories of Model-Driven Engineering (MDE) assignments.
Our approach is based on the adaptation of the Locality Sensitive Hashing, an approximate nearest neighbor search mechanism, to the modeling technical space. We evaluate our approach on a real use case consisting of two repositories containing 10 years of student answers to MDE course assignments.
We have found that: (i) effectively, plagiarism occurred on the aforementioned course assignments (ii) our tool was able to efficiently detect them.
Plagiarism detection must be integrated into the toolset and activities of MDE instructors in order to correctly evaluate students.