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Dolos: Language‐agnostic plagiarism detection in source code.

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Title: Dolos: Language‐agnostic plagiarism detection in source code.
Authors: Maertens, Rien, Van Petegem, Charlotte, Strijbol, Niko, Baeyens, Toon, Jacobs, Arne Carla, Dawyndt, Peter, Mesuere, Bart
Source: Journal of Computer Assisted Learning; Aug2022, Vol. 38 Issue 4, p1046-1061, 16p
Subject Terms: ONLINE education, COMPUTER software, EXPERIMENTAL design, STUDENT cheating, PLAGIARISM, LEARNING strategies, SURVEYS, RESEARCH funding, PROGRAMMING languages, EDUCATIONAL outcomes, COVID-19 pandemic
Abstract: Background: Learning to code is increasingly embedded in secondary and higher education curricula, where solving programming exercises plays an important role in the learning process and in formative and summative assessment. Unfortunately, students admit that copying code from each other is a common practice and teachers indicate they rarely use plagiarism detection tools. Objectives: We want to lower the barrier for teachers to detect plagiarism by introducing a new source code plagiarism detection tool (Dolos) that is powered by state‐of‐the art similarity detection algorithms, offers interactive visualizations, and uses generic parser models to support a broad range of programming languages. Methods: Dolos is compared with state‐of‐the‐art plagiarism detection tools in a benchmark based on a standardized dataset. We describe our experience with integrating Dolos in a programming course with a strong focus on online learning and the impact of transitioning to remote assessment during the COVID‐19 pandemic. Results and Conclusions: Dolos outperforms other plagiarism detection tools in detecting potential cases of plagiarism and is a valuable tool for preventing and detecting plagiarism in online learning environments. It is available under the permissive MIT open‐source license at https://dolos.ugent.be. Implications: Dolos lowers barriers for teachers to discover, prove and prevent plagiarism in programming courses. This helps to enable a shift towards open and online learning and assessment environments, and opens up interesting avenues for more effective learning and better assessment. Lay description: What is already known about this topic?: Source code plagiarism is common in programming courses.Students mention lack of checking as one reason to commit plagiarism.Existing plagiarism detection tools are not broadly used in current practice.Current tools support few programming languages. What this paper adds: A user‐friendly plagiarism detection tool that supports many programming languages.Interactive visualizations to discover, prove and prevent plagiarism.Plagiarism detection tools can help to prevent plagiarism for remote assessment. Implications for practice and/or policy: Dolos makes it easier for teachers to detect source code plagiarism.Dolos helps to prevent students from committing source code plagiarism.Interactive visualizations support plagiarism discovery as an exploratory process.Keep in mind that high similarity does not equal proven plagiarism. [ABSTRACT FROM AUTHOR]
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Abstract:Background: Learning to code is increasingly embedded in secondary and higher education curricula, where solving programming exercises plays an important role in the learning process and in formative and summative assessment. Unfortunately, students admit that copying code from each other is a common practice and teachers indicate they rarely use plagiarism detection tools. Objectives: We want to lower the barrier for teachers to detect plagiarism by introducing a new source code plagiarism detection tool (Dolos) that is powered by state‐of‐the art similarity detection algorithms, offers interactive visualizations, and uses generic parser models to support a broad range of programming languages. Methods: Dolos is compared with state‐of‐the‐art plagiarism detection tools in a benchmark based on a standardized dataset. We describe our experience with integrating Dolos in a programming course with a strong focus on online learning and the impact of transitioning to remote assessment during the COVID‐19 pandemic. Results and Conclusions: Dolos outperforms other plagiarism detection tools in detecting potential cases of plagiarism and is a valuable tool for preventing and detecting plagiarism in online learning environments. It is available under the permissive MIT open‐source license at https://dolos.ugent.be. Implications: Dolos lowers barriers for teachers to discover, prove and prevent plagiarism in programming courses. This helps to enable a shift towards open and online learning and assessment environments, and opens up interesting avenues for more effective learning and better assessment. Lay description: What is already known about this topic?: Source code plagiarism is common in programming courses.Students mention lack of checking as one reason to commit plagiarism.Existing plagiarism detection tools are not broadly used in current practice.Current tools support few programming languages. What this paper adds: A user‐friendly plagiarism detection tool that supports many programming languages.Interactive visualizations to discover, prove and prevent plagiarism.Plagiarism detection tools can help to prevent plagiarism for remote assessment. Implications for practice and/or policy: Dolos makes it easier for teachers to detect source code plagiarism.Dolos helps to prevent students from committing source code plagiarism.Interactive visualizations support plagiarism discovery as an exploratory process.Keep in mind that high similarity does not equal proven plagiarism. [ABSTRACT FROM AUTHOR]
ISSN:02664909
DOI:10.1111/jcal.12662