Source code plagiarism detection: The Unix way

Petrík, Juraj; Chudá, Daniela; Steinmuller, Branislav

doi:10.1109/sami.2017.7880355

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…In an attempt to conceal plagiarism, students use many techniques ranging from changing variable names and adding blank lines to breaking functions and adding non-functional code [22]. PyBryt can act as a plagiarism detection tool by automatically converting students' submissions into RI's and comparing other students submissions to it.…”

Section: Plagiarism Detectionmentioning

confidence: 99%

PyBryt: auto-assessment and auto-grading for computational thinking

Christopher¹,

Schalkwyk²,

Gorman³

et al. 2021

Preprint

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We continuously interact with computerized systems to achieve goals and perform tasks in our personal and professional lives. Therefore, the ability to program such systems is a skill needed by everyone. Consequently, computational thinking skills are essential for everyone, which creates a challenge for the educational system to teach these skills at scale and allow students to practice these skills. To address this challenge, we present a novel approach to providing formative feedback to students on programming assignments. Our approach uses dynamic evaluation to trace intermediate results generated by student's code and compares them to the reference implementation provided by their teachers. We have implemented this method as a Python library and demonstrate its use to give students relevant feedback on their work while allowing teachers to challenge their students' computational thinking skills.

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Section: Plagiarism Detectionmentioning

confidence: 99%

PyBryt: auto-assessment and auto-grading for computational thinking

Christopher¹,

Schalkwyk²,

Gorman³

et al. 2021

Preprint

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“…It starts processing given source code files as their original forms and ends with their source code token strings. This staging process was also followed by [35] but with a different set of stages.…”

Section: Related Workmentioning

confidence: 99%

Programming Style on Source Code Plagiarism and Collusion Detection

Karnalim¹,

Kurniawati²

2020

IJC

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This paper utilises programming style on a source code plagiarism and collusion detection to both capture obvious attempts of such academic dishonesty (which characteristics are ignored on most detection techniques) and prioritise non-coincidental similarity to the coincidental one (as only the former can raise suspicion). The technique relies on pairwise programming style similarity to deal with the former and dishonesty probability (how significant is the programming style change between the author’s current submission and previous submissions) to deal with the latter. According to our evaluation, programming style similarity can increase precision since when a code is copied, the programming style can be unconsciously shared (especially for novice students). Dishonesty probability increases not only precision but also recall, f-score, and the resulted similarity degree of suspected pairs; the copied code commonly has different programming style in comparison with the student’s usual style (captured from previous submissions). Our detection technique is comparable to a common technique in academia except that it takes longer processing time as more hints are generated and considered.

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“…Reference [8] presents a method for detecting similarities in language independent source code using standard Unix filter. Reference [9] introduces an approach to identify plagiarism by analyzing the sequence of code submission made by a single student.…”

Section: Introductionmentioning

confidence: 99%

Application of JavaScript Code Similarity Detection for Assessment of Web Programming Assignment

Niswar

2022

EPI-IJE

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Students tend to copy programming assignments from their classmates in programming courses. Students copy codes in various ways, such as changing variable names and code structure order. Lecturers spend much time checking programming assignments, especially when the number of students enrolled in the course is large. They must check whether students have completed their programming assignments individually or copied their classmates' assignments. We developed a JavaScript code similarity detection application for web programming coursework using lexical analysis and Jero Winkler's Algorithm. Our application can detect the level of the students’ programming assignment similarity and assist the lecturer in deciding on plagiarism.

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Source code plagiarism detection: The Unix way

Cited by 9 publications

References 8 publications

PyBryt: auto-assessment and auto-grading for computational thinking

PyBryt: auto-assessment and auto-grading for computational thinking

Programming Style on Source Code Plagiarism and Collusion Detection

Application of JavaScript Code Similarity Detection for Assessment of Web Programming Assignment

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