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 / 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. / MT 2017
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/23505 |
| Date | January 2017 |
| Creators | Obaido, George Rabeshi |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | Online resource (x, 99 leaves), application/pdf, application/pdf |
Page generated in 0.0024 seconds