- Title
- Similarity detection techniques for academic source code plagiarism and collusion: A review
- Creator
- Karnalim, Oscar; Simon,; Chivers, William
- Relation
- IEEE TALE 2019: International Conference on Teaching, Assessment and Learning for Engineering. IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) (Yogyakarta, Indonesia 08-11 April, 2019)
- Publisher Link
- http://dx.doi.org/10.1109/TALE48000.2019.9225953
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2020
- Description
- Source code plagiarism and collusion are continuing problems in academia. To deal with these issues, lecturers are often aided by automated code similarity detection techniques or tools. Students' programs are filtered by these, and suspicious groups of programs are displayed to the markers for further investigation. As the detection techniques are many and varied, it can be demanding to choose the most suitable one for a particular teaching context. This paper summarises the mechanisms by which each of these techniques works, compiled from publications listed by Google Scholar and one or more of the ACM digital library, IEEE Xplore digital library, ScienceDirect, Scopus, and the references of already listed publications. The review is intended as a guideline for lecturers seeking to choose the most suitable technique, and for researchers who are seeking to understand the current trends and the possible research gaps in this topic.
- Subject
- source code similarity detection; plagiarism; collustion; academic integrity; programming; computing education
- Identifier
- http://hdl.handle.net/1959.13/1438370
- Identifier
- uon:40584
- Identifier
- ISBN:9781728126654
- Language
- eng
- Reviewed
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