Efficient plagiarism detection for software modeling assignments. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Martínez, Salvador; Wimmer, Manuel; Cabot, Jordi
• Is Part Of: Computer science education. Volume 30:Issue 2(2020)
• Page Start: 187
• Page End: 215
• Publication Date: 2020-04-02
• Subjects: Model-driven engineering -- robust hashing -- locality sensitive hashing -- clustering -- plagiarism detection; Computer science -- Study and teaching -- Periodicals; 004
• DOI: 10.1080/08993408.2020.1711495
• Languages: English
• ISSNs: 0899-3408

Abstract

ABSTRACT: Background and Context: 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. Objective: To provide an efficient mechanism for the detection of plagiarism in repositories of Model-Driven Engineering (MDE) assignments. Method: 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. Findings: We have found that: ( i ) effectively, plagiarism occurred on the aforementioned course assignments ( i i ) our tool was able to efficiently detect them. Implications: Plagiarism detection must be integrated into the toolset and activities of MDE instructors in order to correctly evaluate students