Structural analysis of source code plagiarism using graphs

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dc.contributor.author Obaido, George Rabeshi
dc.date.accessioned 2017-12-18T12:04:20Z
dc.date.available 2017-12-18T12:04:20Z
dc.date.issued 2017
dc.identifier.citation Obaido, George Rabeshi (2017) Structural analysis of source code plagiarism using graphs, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/23505>
dc.identifier.uri http://hdl.handle.net/10539/23505
dc.description A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg in fulfillment of the requirements for the degree of Master of Science. May 2017 en_ZA
dc.description.abstract Plagiarism is a serious problem in academia. It is prevalent in the computing discipline where students are expected to submit source code assignments as part of their assessment; hence, there is every likelihood of copying. Ideally, students can collaborate with each other to perform a programming task, but it is expected that each student submit his/her own solution for the programming task. More so, one might conclude that the interaction would make them learn programming. Unfortunately, that may not always be the case. In undergraduate courses, especially in the computer sciences, if a given class is large, it would be unfeasible for an instructor to manually check each and every assignment for probable plagiarism. Even if the class size were smaller, it is still impractical to inspect every assignment for likely plagiarism because some potentially plagiarised content could still be missed by humans. Therefore, automatically checking the source code programs for likely plagiarism is essential. There have been many proposed methods that attempt to detect source code plagiarism in undergraduate source code assignments but, an ideal system should be able to differentiate actual cases of plagiarism from coincidental similarities that usually occur in source code plagiarism. Some of the existing source code plagiarism detection systems are either not scalable, or performed better when programs are modified with a number of insertions and deletions to obfuscate plagiarism. To address this issue, a graph-based model which considers structural similarities of programs is introduced to address cases of plagiarism in programming assignments. This research study proposes an approach to measuring cases of similarities in programming assignments using an existing plagiarism detection system to find similarities in programs, and a graph-based model to annotate the programs. We describe experiments with data sets of undergraduate Java programs to inspect the programs for plagiarism and evaluate the graph-model with good precision. An evaluation of the graph-based model reveals a high rate of plagiarism in the programs and resilience to many obfuscation techniques, while false detection (coincident similarity) rarely occurred. If this detection method is adopted into use, it will aid an instructor to carry out the detection process conscientiously. en_ZA
dc.format.extent Online resource (x, 99 leaves)
dc.language.iso en en_ZA
dc.subject.lcsh Source code (Computer science)
dc.subject.lcsh Plagiarism
dc.subject.lcsh Academic writing
dc.title Structural analysis of source code plagiarism using graphs en_ZA
dc.type Thesis en_ZA
dc.description.librarian MT 2017 en_ZA


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