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Suitability of BlackBox dataset for style analysis in detection of source code plagiarism
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Mirza, Olfat, Joy, Mike and Cosma, Georgina (2017) Suitability of BlackBox dataset for style analysis in detection of source code plagiarism. In: 2017 Seventh International Conference on Innovative Computing Technology (INTECH), Luton, UK, 16-18 Aug 2017 pp. 90-94. ISBN 9781509039890. doi:10.1109/INTECH.2017.8102424
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WRAP-suitability-blackbox-dataset-style-analysis-detection-source-code-plagiarism-Joy-2018.pdf - Accepted Version - Requires a PDF viewer. Download (664Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/INTECH.2017.8102424
Abstract
Plagiarism is one of the most common problem that has been increasing in the field of higher education. Many research papers have highlighted the issue of plagiarism in context to its detection and source that is often obtained from the text books and online sources, there is a variety of easy ways for students to copy others' work. Coding style can be used to detect source code plagiarism because it relates to programmer personality but does not affect the logic of a program, thus offering a way to differentiate between different code authors. The immediate objective of this paper is to identify whether a data set consisting of student programming assignments is rich enough to apply coding style metrics on in order to detect similarities between code sequences, and we use the BlackBox data set as a case study.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
| Divisions: | Faculty of Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Plagiarism -- Software, Source code (Computer science), Java (Computer program language) | ||||||
| Publisher: | IEEE | ||||||
| ISBN: | 9781509039890 | ||||||
| Book Title: | 2017 Seventh International Conference on Innovative Computing Technology (INTECH) | ||||||
| Official Date: | 9 November 2017 | ||||||
| Dates: |
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| Page Range: | pp. 90-94 | ||||||
| DOI: | 10.1109/INTECH.2017.8102424 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 2017 Seventh International Conference on Innovative Computing Technology (INTECH) | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Luton, UK | ||||||
| Date(s) of Event: | 16-18 Aug 2017 |
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