An empirical approach for detecting program similarity and plagiarism within a university programming environment
Integrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Plagiarism-hiding transformations are usually cosmetic: they alter source code appearance and structure while preserving the original program's behaviour.
“Such source code transformations are typically cosmetic in nature, in that they change the appearance and structure of the source code [6], [7], [10], but retain the original behaviour of the plagiarised assignment.”
From Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity (2021) · cites this paper as [10]
Faidhi and Robinson's taxonomy classifies plagiarism-hiding source code transformations into six levels.
“This taxonomy categorises transformations into six levels: L1) Changes to comments and indentation.L2) Changes to identifiers.L3) Changes in declarations (e.g.declaring extra constants, changing the order of functions and variables).L4) Modifying functions (e.g.modifying signature, merging and creating new functions).L5) Changing program statements to semantic equivalents (e.g. for to while, if to”
From Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity (2021) · cites this paper as Faidhi and Robinson [10]
Structural transformations alter source code appearance and structure but not behaviour.
“Such transformations modify the appearance and structure of source code, but not behaviour.”
From Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity (2021) · cites this paper as [10]
Structural transformations correspond to L3 and higher transformations in Faidhi and Robinson's taxonomy.
“Structural transformations are equivalent to L3+ transformations from Faidhi and Robinson's [10] taxonomy.”
From Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity (2021) · cites this paper as [10]
All implemented source code transformations are referenced from prior literature and conform to the first 5 levels of Faidhi and Robinson's transformation taxonomy.
“To ensure the generated programs are representative of undergraduate plagiarism, all implemented source code transformations (listed in table 4) are referenced from prior literature [7], [10], [50], [51], and conform to the first 5 levels of Faidhi and Robinson's [10] transformation taxonomy.”
From Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity (2021) · cites this paper as [10]
SPPlagiarise provides 19 source code transformations, which correspond to levels L1 through L5 of the taxonomy of Faidhi and Robinson.
“SPPlagiarise implements a total of 19 source code transformations conforming to L1-L5 of Faidhi and Robinson's [10] taxonomy.”
From Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity (2021) · cites this paper as [10]
Cites
Donaldson, J. L., Lancaster, A.-M., & Sposato, P. H. (1981) · ACM SIGCSE Bulletin · doi:10.1145/953049.800955 · Cited by 38 · see citing works
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Finding Plagiarisms among a Set of Programs with JPlag
Malpohl, G., Prechelt, L., & Philippsen, M. (2002) · Verlag der Technischen Universität Graz · doi:10.3217/jucs-008-11-1016 · Cited by 56 · see citing works
Computer algorithms for plagiarism detection
Parker, A., & Hamblen, J. O. (1989) · IEEE Transactions on Education · doi:10.1109/13.28038 · Cited by 216 · see citing works
The changing forms and expectations of peer review
Horbach, S. P. J. M., & Halffman, W. (. W. (2018) · Research Integrity and Peer Review · doi:10.1186/s41073-018-0051-5 · Cited by 179 · see citing works
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
A comparison of code similarity analysers
Ragkhitwetsagul, C., Krinke, J., & Clark, D. (2018) · Empirical Software Engineering · doi:10.1007/s10664-017-9564-7 · Cited by 122 · see citing works
Source-code Similarity Detection and Detection Tools Used in Academia: A Systematic Review
Novak, M., Joy, M., & Kermek, D. (2019) · ACM Transactions on Computing Education · doi:10.1145/3313290 · Cited by 117 · see citing works
Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity
Cheers, H., Lin, Y., & Smith, S. P. (2021) · IEEE Access · doi:10.1109/access.2021.3069367 · Cited by 59 · see citing works
“Such source code transformations are typically cosmetic in nature, in that they change the appearance and structure of the source code [6], [7], [10], but retain the original behaviour of the plagiarised assignment.”
BackgroundMethodInfluentialAutomatic Source Code Plagiarism Detection
Kustanto, C., & Liem, I. (2009) · 2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing · doi:10.1109/snpd.2009.62 · Cited by 58 · see citing works
Karnalim, O. (2016) · 2016 International Conference on Information & Communication Technology and Systems (ICTS) · doi:10.1109/icts.2016.7910274 · Cited by 46 · see citing works
Desktop Tools for Offline Plagiarism Detection in Computer Programs
MOZGOVOY, M. (2006) · Informatics in Education · doi:10.15388/infedu.2006.08 · Cited by 46 · see citing works
Sraka, D., & Kaucic, B. (2009) · Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces · doi:10.1109/iti.2009.5196127 · Cited by 42 · see citing works
Fooling MOSS Detection with Pretrained Language Models
Biderman, S., & Raff, E. (2022) · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · doi:10.1145/3511808.3557079 · Cited by 37 · see citing works
Detecting Pervasive Source Code Plagiarism through Dynamic Program Behaviours
Cheers, H., Lin, Y., & Smith, S. P. (2020) · Proceedings of the Twenty-Second Australasian Computing Education Conference · doi:10.1145/3373165.3373168 · Cited by 13 · see citing works
A Novel Graph-Based Program Representation for Java Code Plagiarism Detection
Cheers, H., & Lin, Y. (2020) · Proceedings of the 3rd International Conference on Software Engineering and Information Management · doi:10.1145/3378936.3378960 · Cited by 13 · see citing works
- Authors
- Faidhi, J. A. W., & Robinson, S. K.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 1987-01
- Volume
- 11
- Issue
- 1
- Pages
- 11-19
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W1971922616
- Cited by
- 198
- References
- 1
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 6 · 2023: 7 · 2022: 11 · 2021: 16 · 2020: 13 · 2019: 15 · 2018: 11 · 2017: 7 · 2016: 10 · 2015: 6 · 2014: 9 · 2013: 6 · 2012: 17
- FWCI
- 5.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Education (publishedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- plagiarism detection, code similarity, source code plagiarism, programming education
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