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Cited by 10Academic integrity and plagiarismSoftware Engineering ResearchOnline Learning and Analytics

The DOI matches its Crossref record; 16 of the 20 references it cites were found in a registry or an index, and 4 are not checked yet.

Discovering and exploring cases of educational source code plagiarism with Dolos

Open access
Maertens, Rien, Van Neyghem, Maarten, Geldhof, Maxiem, Van Petegem, Charlotte, Strijbol, Niko, Dawyndt, Peter, Mesuere, Bart·2024·SoftwareX
DOI 10.1016/j.softx.2024.101755
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Abstract

Source code plagiarism is a significant issue in educational practice, and educators need user-friendly tools to cope with such academic dishonesty. This article introduces the latest version of Dolos, a state-of-the-art ecosystem of tools for detecting and preventing plagiarism in educational source code. In this new version, the primary focus has been on enhancing the user experience. Educators can now run the entire plagiarism detection pipeline from a new web app in their browser, eliminating the need for any installation or configuration. Completely redesigned analytics dashboards provide an instant assessment of whether a collection of source files contains suspected cases of plagiarism and how widespread plagiarism is within the collection. The dashboards support hierarchically structured navigation to facilitate zooming in and out of suspect cases. Clusters are an essential new component of the dashboard design, reflecting the observation that plagiarism can occur among larger groups of students. To meet various user needs, the Dolos software stack for source code plagiarism detection now includes a self-hostable web app, a JSON application programming interface (API), a command line interface (CLI), a JavaScript library and a preconfigured Docker container. Clear documentation and a free-to-use instance of the web app can be found at https://dolos.ugent.be. The source code is also available on GitHub.

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Integrity signals

Registry

Crossref · 10.1016/j.softx.2024.101755

Cites

16 of 20 references verified0 citation errors · 0 not checkable

Statistics

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  • To prevent plagiarism, we use Dolos to compare the agent's submitted code against the top 50 associated notebooks from the relevant Kaggle competition.

    “To prevent plagiarism, we use the source code plagiarism detection tool Dolos (Maertens et al., 2024) to compare the agent's submitted code against the top 50 associated notebooks from the relevant Kaggle competition.”

    From MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (2024) · cites this paper as Maertens et al., 2024

  • No detected cases of plagiarism were found in submissions with a similarity score above 60%.

    “We surface any submission file with a similarity score above 60% for human review of potential plagiarism, finding no detected cases of plagiarism.”

    From MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (2024) · cites this paper as Maertens et al., 2024

    The 'Step 2: exploring analysis results' section describes an adjustable similarity threshold and educator review of suspected plagiarism. It does not report a 60% cutoff or the outcome of reviewing submissions above that cutoff. Searches for those terms found no passage establishing that no plagiarism cases were detected. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • No submission has a similarity score above 60%.

    “We observe that no submission has a similarity score above 60%.”

    From MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (2024) · cites this paper as Maertens et al., 2024

    The Step 2: exploring analysis results passages describe pairwise similarity scores and adjustable thresholds, but give no maximum score for submissions. A search for “60%” and maximum similarity found no passage establishing that no submission exceeded 60%. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

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Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Jiffriya, 2021, Plagiarism detection tools and techniques: A comprehensive survey, J Sci-FAS-SEUSL, 02, 47

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Metadata

Authors
Maertens, R., Van Neyghem, M., Geldhof, M., Van Petegem, C., Strijbol, N., Dawyndt, P., & Mesuere, B.
Affiliations
Ghent University (BE)
Corresponding author
Maertens, Rien
ORCID
Venue
SoftwareX
Publisher
Elsevier BV
Published
2024-05
Volume
26
Pages
101755
ISSN
2352-7110 (ISSN-L)
DOI
10.1016/j.softx.2024.101755
OpenAlex
W4391949005
Cited by
9
References
17
Citations by year
2026: 1 · 2025: 6 · 2024: 2
Type
software paper
Language
en
Open access
Licence
Locations
  • SoftwareX (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University) (cc-by)
Funders
  • Vlaamse regering
  • Universiteit Gent
  • Fonds Wetenschappelijk Onderzoek Vlaanderen (I002819N, I000323N)
Topics
  • Social Sciences › Education › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
source code plagiarism, educational programming, plagiarism detection, dolo, academic dishonesty, analytics dashboard, user experience, Docker containers, API

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