Plagiarism Detection: A Tool Survey and Comparison
Open accessAbstract
We illustrate the state of the art in software plagiarism detection tools by comparing their features and testing them against a wide range of source codes. The source codes were edited according to several types of plagiarism to show the tools accuracy at detecting each type. The decision to focus our research on plagiarism of programming languages is two fold: on one hand, it is a challenging case-study since programming languages impose a structured writing style; on the other hand, we are looking for the integration of such a tool in an Automatic-Grading System (AGS) developed to support teachers in the context of Programming courses. Besides the systematic characterisation of the underlying problem domain, the tools were surveyed with the objective of identifying the most successful approach in order to design the aimed plugin for our AGS.
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All source code plagiarism detection tools that are known to be available use only structural measures of similarity.
“Furthermore, all known available SCPDTs implement structural measures of similarity only.”
From Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity (2021) · cites this paper as [8]
Section 2.2, “Approaches” (page unavailable), distinguishes attribute-based, token-based, and structure-based methods rather than saying all tools use structural measures only. It describes token-based tools that add some structure-dependent modifications; Section 4.4 identifies Marble as using token comparison. These passages contradict the claim’s “all” and “only.” (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredEvery source code plagiarism detection tool proposed so far uses structural or semantic measures of similarity.
“All known proposed SCPDTs implement structural or semantic measures of similarity.”
From Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity (2021) · cites this paper as [8]
Section 2.2 (page not supplied) describes attribute-based and token-based methods as well as structure-based methods. Its examples include comparisons using code size and token hashes, contradicting the claim that all proposed tools use structural or semantic similarity measures. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredRecent literature reviews list a range of structural and/or semantic source code plagiarism detection tools but none that are behavioural, which supports the absence of comparable approaches.
“The lack of similar approaches to SCPD is supported by the recent literature reviews [8], [9] that identify various structural and/or semantic SCPDTs, however, fail to identify any behavioural SCPDTs.”
From Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity (2021) · cites this paper as [8]
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Cites
Clone detection using abstract syntax trees
Baxter, I. D., Yahin, A., Moura, L., Sant'Anna, M., & Bier, L. (2002) · Proceedings. International Conference on Software Maintenance (Cat. No. 98CB36272) · doi:10.1109/icsm.1998.738528 · Cited by 1,277 · see citing works
Schleimer, S., S, W. D., & Aiken, A. (2003) · Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data · doi:10.1145/872757.872770 · Cited by 1,099 · see citing works
GPLAG: detection of software plagiarism by program dependence graph analysis
Liu, C., Chen, C., Han, J., & Yu, P. S. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150522 · Cited by 422 · see citing works
Plagiarism in programming assignments
Joy, M., & Luck, M. (1999) · IEEE Transactions on Education · doi:10.1109/13.762946 · Cited by 299 · see citing works
Wise, M. J. (1996) · ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/236452.236525 · Cited by 296 · see citing works
Towards a Definition of Source-Code Plagiarism
Cosma, G., & Joy, M. (2008) · IEEE Transactions on Education · doi:10.1109/te.2007.906776 · Cited by 146 · see citing works
JPlag: finding plagiarisms among a set of programs
Prechelt, L., Malpohl, G., & Philippsen, M. (2000) · Karlsruhe · doi:10.5445/ir/542000 · Cited by 144 · see citing works
A technique for isolating differences between files
Heckel, P. (1978) · Communications of the ACM · doi:10.1145/359460.359467 · Cited by 124 · see citing works
Detection of similarities in student programs
Wise, M. J. (1992) · ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/134510.134564 · Cited by 88 · see citing works
Ahtiainen, A., Surakka, S., & Rahikainen, M. (2006) · Koli Calling International Conference on Computing Education Research · doi:10.1145/1315803.1315831 · Cited by 84 · see citing works
A comparison of plagiarism detection tools
Hage, J., & Rademaker, P. (2010) · Cited by 62 · see citing works
Software metrics and plagiarism detection
Whale, G. (1990) · Journal of Systems and Software · doi:10.1016/0164-1212(90)90118-6 · Cited by 40 · see citing works
Code Comparison System based on Abstract Syntax Tree
Cui, B., Li, J., Guo, T., Wang, J., & Ma, D. (2010) · IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT) · doi:10.1109/icbnmt.2010.5705174 · Cited by 37 · see citing works
Running Karp-Rabin Matching and Greedy String Tiling
Wise, M. J. (2003) · Cited by 33 · see citing works
Who’s the Thief? Automatic Detection of the Direction of Plagiarism
Grozea, C., & Popescu, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12116-6_59 · Cited by 31 · see citing works
Detection of source code similitude in academic environments
Bejarano, A., García, L., & Zurek, E. E. (2013) · Computer Applications in Engineering Education · doi:10.1002/cae.21571 · Cited by 27 · see citing works
The Source Code Plagiarism Detection Using AST
Li, X., & Zhong, X. J. (2010) · Information Processing and Trusted Computing · doi:10.1109/iptc.2010.90 · Cited by 21 · see citing works
Linguistic and Statistical Traits Characterising Plagiarism
Chong, M., & Specia, L. (2012) · Cited by 15 · see citing works
Program Analysis and Evaluation using Quimera
Fonte, D., Boas, I. V., da Cruz, D., Gançarski, A. L., & Henriques, P. R. (2012) · International Conference on Enterprise Information Systems · doi:10.5220/0004001702090219 · Cited by 7 · see citing works
Self-citationMeasuring Whitespace Patterns as an Indication of Plagiarism
Shay, I., Baer, N., & Zeidman, R. (2010) · Cited by 4 · see citing works
Flagged first, then by citations
1 of the 21 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Aleksi Ahtiainen, Sami Surakka, and Mikko Rahikainen. Plaggie: GNU-licensed source code plagiarism detection engine for Java exercises. In Proceedings of 6th Koli Calling Intern. Conference on Comp. Ed. Research, pages 141-142, New York, USA, 2006. ACM.
- Dick Grune and Matty Huntjens. Het detecteren van kopieën bij informatica-practica. Informatie, 31(11):864-867, 1989.
- Michael J. Wise. YAP3: Improved detection of similarities in computer program and other texts. In SIGCSEB: SIGCSE Bulletin, pages 130-134. ACM Press, 1996.
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Sorted by citations
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
SOTorrent: Reconstructing and Analyzing the Evolution of Stack Overflow Posts
Baltes, S., Dumani, L., Treude, C., & Diehl, S. (2018) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.48550/arxiv.1803.07311 · Cited by 107 · see citing works
Testing of support tools for plagiarism detection
Foltýnek, T., Dlabolová, D., Anohina-Naumeca, A., Razı, S., Kravjar, J., Kamzola, L., Guerrero-Dib, J., Çelik, Ö., & Weber-Wulff, D. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-00192-4 · Cited by 15 · 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
“The lack of similar approaches to SCPD is supported by the recent literature reviews [8], [9] that identify various structural and/or semantic SCPDTs, however, fail to identify any behavioural SCPDTs.”
ResultPlagiarism in Take-home Exams: Help-seeking, Collaboration, and Systematic Cheating
Hellas, A., Leinonen, J., & Ihantola, P. (2017) · Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3059009.3059065 · Cited by 50 · see citing works
Kanjirangat, V., & Gupta, D. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.01.008 · Cited by 49 · see citing works
Review of source-code plagiarism detection in academia
Novak, M. (2016) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2016.7522248 · Cited by 41 · see citing works
Ljubovic, V., & Pajic, E. (2020) · IEEE Access · doi:10.1109/access.2020.2996146 · Cited by 38 · see citing works
A Comparison of Three Popular Source code Similarity Tools for Detecting Student Plagiarism
Ahadi, A., & Mathieson, L. (2019) · Australasian Computing Education Conference · doi:10.1145/3286960.3286974 · Cited by 29 · see citing works
Li, L., Simek, O., Lai, A., Daggett, M., Dagli, C. K., & Jones, C. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622189 · Cited by 26 · see citing works
Clustering Student Programming Assignments to Multiply Instructor Leverage
Yin, H., Moghadam, J., & Fox, A. (2015) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2724660.2728695 · Cited by 26 · see citing works
Syntax-based Improvements to Plagiarism Detectors and their Evaluations
Nichols, L., Dewey, K., Emre, M., Chen, S., & Hardekopf, B. (2019) · Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3304221.3319789 · Cited by 22 · see citing works
Academic Integrity in Non-Text Based Disciplines
Simon (2016) · Springer eBooks · doi:10.1007/978-981-287-098-8_61 · Cited by 22 · see citing works
Unsupervised Learning Based Approach for Plagiarism Detection in Programming Assignments
Yasaswi, J., Kailash, S., Chilupuri, A., Purini, S., & Jawahar, C. V. (2017) · Innovations in Software Engineering Conference (ISEC) · doi:10.1145/3021460.3021473 · Cited by 19 · see citing works
A Quantitative Comparison of Program Plagiarism Detection Tools
Heres, D., & Hage, J. (2017) · Computer Science Education Research Conference · doi:10.1145/3162087.3162101 · Cited by 18 · see citing works
INFOSHIELD: Generalizable Information-Theoretic Human-Trafficking Detection
Lee, M.-C., Vajiac, C., Kulshrestha, A., Lévy, S., Park, N., Jones, C., Reihaneh Rabbany, K., & Faloutsos, C. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00101 · Cited by 17 · see citing works
Evaluating plagiarism detection software for introductory programming assignments
Modiba, P., Pieterse, V., & Haskins, B. (2016) · Computer Science Education Research Conference · doi:10.1145/2998551.2998558 · Cited by 17 · see citing works
Cheers, H., Lin, Y., & Smith, S. P. (2021) · Empirical Software Engineering · doi:10.1007/s10664-021-09990-4 · Cited by 14 · see citing works
Automatic programming error class identification with code plagiarism-based clustering
Combéfis, S., & Schils, A. (2016) · doi:10.1145/2993270.2993271 · Cited by 14 · see citing works
COAT: Code Obfuscation Tool to Evaluate the Performance of Code Plagiarism Detection Tools
Ko, S., Choi, J., & Kim, H. (2017) · International Conference on Software Security and Assurance (ICSSA) · doi:10.1109/icssa.2017.29 · Cited by 11 · see citing works
Improving Plagiarism Detection Using Genetic Algorithm
Pajic, E., & Ljubovic, V. (2019) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro.2019.8756744 · Cited by 11 · see citing works
Source code plagiarism detection: The Unix way
Petrik, J., Chudá, D., & Steinmuller, B. (2017) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami.2017.7880355 · Cited by 9 · see citing works
T-norms or t-conorms? How to aggregate similarity degrees for plagiarism detection
Bartoszuk, M., & Ga̧golewski, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107427 · Cited by 9 · see citing works
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Simon (2015) · Springer eBooks · doi:10.1007/978-981-287-079-7_61-1 · Cited by 9 · see citing works
A unified approach to automate the usage of plagiarism detection tools in programming courses
Portillo‐Dominguez, A. O., Ayala-Rivera, V., Murphy, E., & Murphy, J. (2017) · International Conference on Computer Science and Education · doi:10.1109/iccse.2017.8085456 · Cited by 7 · see citing works
CPLAG: Efficient plagiarism detection using bitwise operations
Jain, S., Kaur, P., Goyal, M., & Gopinathan, D. (2017) · International Conference on Contemporary Computing · doi:10.1109/ic3.2017.8284313 · Cited by 6 · see citing works
A Real-time Plagiarism Detection Tool for Computer-based Assessments
Jeske, H. J., Lall, M., & Kogeda, O. P. (2018) · Journal of Information Technology Education Innovations in Practice · doi:10.28945/3963 · Cited by 6 · see citing works
CrossFix: Resolution of GitHub issues via similar bugs recommendation
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Sierra, J. L., Leal, J. P., & Simões, A. (2015) · Communications in computer and information science · doi:10.1007/978-3-319-27653-3 · Cited by 5 · see citing works
Chinedu, U. C., Ikerionwu, C., & Nwokonkwo, O. (2020) · International Journal of Scientific and Research Publications · doi:10.29322/ijsrp.10.03.2020.p9969 · Cited by 5 · see citing works
Liu, Z., Xing, W., Jiao, X., Li, C., & Zhu, W. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13385-z · Cited by 6 · see citing works
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Novak, M., Kermek, D., & Joy, M. S. (2018) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro.2018.8400147 · Cited by 3 · see citing works
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Ho, P. H., Vo, T. H., & Nguyen, N. A. T. (2017) · International Conference on System Science and Engineering · doi:10.1109/icsse.2017.8030873 · Cited by 3 · see citing works
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- Authors
- Martins, V. T., Fonte, D., Henriques, P. R., & da Cruz, D.
- Affiliations
- University of Minho (PT)
- Venue
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Publisher
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Published
- 2014
- OpenAlex
- W2096498787
- Cited by
- 53
- References
- 21
- Citations by year
- 2025: 1 · 2024: 7 · 2023: 3 · 2022: 1 · 2021: 6 · 2020: 5 · 2019: 6 · 2018: 5 · 2017: 8 · 2016: 6 · 2015: 5
- FWCI
- 17.66
- Type
- journal article
- Language
- en
- Open access
- green
- drops.dagstuhl.de
- Licence
- Locations
- DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) (submittedVersion, cc-by)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
- OASIcs : OpenAccess Series in Informatics (cc-by)
- Funders
- Fundação para a Ciência e a Tecnologia (PEst-OE/EEI/UI0752/2014)
- 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
- software plagiarism detection, plagiarism detection software, tool comparison, source code plagiarism, automated grading, programming education
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