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Efficient plagiarism detection for software modeling assignments
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DOI：10.1080/08993408.2020.1711495.png)
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Abstract
En 中文
Reports suggest that plagiarism is a common occurrence in universities. Students typically refer to public repositories or copies of previous years' exercises in order to complete all or part of their assignments. While plagiarism detection mechanisms exist for textual documents, this is less so for software components, especially for non-code related ones such as software design artefacts like models, metamodels or model transformations. Indeed, these artefacts are complex and require more sophisticated and computationally expensive comparison mechanism than text documents. Given the prominence of model-based development approaches in current software projects, ignoring plagiarism puts at risk the correct and fair evaluation of students. To cope with this, we explore the application of the locality sensitive hashing technique to the detection of plagiarism in artifact repositories. We validate the feasibility of our approach by providing a prototype implementation and its evaluation over a large repository of student solutions to modeling course assignments .
Keywords:
Model-driven engineering
robust hashing
locality sensitive hashing
clustering
plagiarism detection
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