Syntax-based Improvements to Plagiarism Detectors and their Evaluations

Nichols, Lawton; Dewey, Kyle; Emre, Mehmet; Chen, Sitao; Hardekopf, Ben

doi:10.1145/3304221.3319789

Cited by 17 publications

(13 citation statements)

References 11 publications

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“…In addition to Moss, we aimed to collect all of the related systems described in Section 8. This effort was largely unsuccessful, with three exceptions: JPlag [Prechelt et al 2002], Sherlock [Joy and Luck 1999], and Fett [Nichols et al 2019]. JPlag and Sherlock are the only tools, aside from Moss, our survey respondents reported using.…”

Section: Other Plagiarism Detection Toolsmentioning

confidence: 98%

“…Unfortunately, this effort was largely unsuccessful, as almost none of these systems are available for use. We reached out to the authors of the cited papers, and were unsuccessful in obtaining any of their systems, with three exceptions: JPlag [Prechelt et al 2002], Sherlock [Joy and Luck 1999], and Fett [Nichols et al 2019]. We focus our evaluation on JPlag, since it was the second-most popular tool cited by our survey respondents.…”

Section: Rq6: Mossad Vs Other Plagiarism Detectorsmentioning

confidence: 99%

“…Although none of our survey respondents reported using the tool, Fett is the only additional publicly-available plagiarism detection tool the authors could find. In fact, one of the major pillars of Fett is that it is open-source and easily accessible, along with being language-agnostic and highly accurate [Nichols et al 2019].…”

Section: Code Typementioning

confidence: 99%

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Mossad: Defeating Software Plagiarism Detection

Devore-McDonald¹,

Berger²

2020

Preprint

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Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in use together with the rise in enrollments in computer science programs and the widespread availability of code on-line. Educators rely on the robustness of plagiarism detection tools; the working assumption is that the effort required to evade detection is as high as that required to actually do the assigned work.This paper shows this is not the case. It presents an entirely automatic program transformation approach, Mossad, that defeats popular software plagiarism detection tools. Mossad comprises a framework that couples techniques inspired by genetic programming with domain-specific knowledge to effectively undermine plagiarism detectors. Mossad is effective at defeating four plagiarism detectors, including Moss [Schleimer et al. 2003] and JPlag [Prechelt et al. 2002]. Mossad is both fast and effective: it can, in minutes, generate modified versions of programs that are likely to escape detection. More insidiously, because of its nondeterministic approach, Mossad can, from a single program, generate dozens of variants, which are classified as no more suspicious than legitimate assignments. A detailed study of Mossad across a corpus of real student assignments demonstrates its efficacy at evading detection. A user study shows that graduate student assistants consistently rate Mossad-generated code as just as readable as authentic student code. This work motivates the need for both research on more robust plagiarism detection tools and greater integration of naturally plagiarism-resistant methodologies like code review into computer science education.CCS Concepts: • Software and its engineering → Genetic programming; Collaboration in software development; • Social and professional topics → Computer science education.

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Section: Other Plagiarism Detection Toolsmentioning

confidence: 98%

Section: Rq6: Mossad Vs Other Plagiarism Detectorsmentioning

confidence: 99%

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Mossad: Defeating Software Plagiarism Detection

Devore-McDonald¹,

Berger²

2020

Preprint

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“…A similar technique is used by MOSS [46]. Aiming for higher efectiveness, a number of techniques use more complex representations such as syntax trees [38] and program dependency graphs [35].…”

Section: Code Similarity Detectorsmentioning

confidence: 99%

Educating Students about Programming Plagiarism and Collusion via Formative Feedback

Karnalim

Simón²,

Chivers

et al. 2022

ACM Trans. Comput. Educ.

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To help address programming plagiarism and collusion, students should be informed about acceptable practices and about program similarity, both coincidental and non-coincidental. However, current approaches are usually manual, brief, and delivered well before students are in a situation where they might commit academic misconduct. This paper presents an assessment submission system with automated, personalized, and timely formative feedback that can be used in institutions that apply some leniency in early instances of plagiarism and collusion. If a student’s submission shares coincidental or non-coincidental similarity with other submissions, personalized similarity reports are generated for the involved submissions and the students are expected to explain the similarity and resubmit the work. Otherwise, a report simulating similarities is sent just to the author of the submitted program to enhance their knowledge. Results from two quasi-experiments involving two academic semesters suggest that students with our approach are more aware of programming plagiarism and collusion, including the futility of some program disguises. Further, their submitted programs have lower similarity even at the level of program flow, suggesting that they are less likely to have engaged in programming plagiarism and collusion. Student behavior while using the system is also analyzed based on the statistics of the generated reports and student justifications for the reported similarities.

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“…In this section we provide a brief overview of various approaches proposed for the detection of plagiarism and paraphrase plagiarism. In particular, approaches based on character and word n-gram similarity (Bensalem et al 2019;Sánchez-Vega et al 2017), vector space models (Sanchez-Perez et al 2014), natural language processing (Chong 2013;Kanjirangat and Gupta 2018) machine translation similarity metrics (Madnani et al 2012) and alignment algorithms (Nichols et al 2019) have been successfully applied towards plagiarism detection. Despite these advances, plagiarism detection when text has been paraphrased remains a challenge due to limited success in measuring semantic overlap (Carmona et al 2018).…”

Section: Plagiarism Detectionmentioning

confidence: 99%

Paraphrase type identification for plagiarism detection using contexts and word embeddings

Alvi

Stevenson

Clough

2021

Int J Educ Technol High Educ

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Paraphrase types have been proposed by researchers as the paraphrasing mechanisms underlying acts of plagiarism. Synonymous substitution, word reordering and insertion/deletion have been identified as some of the common paraphrasing strategies used by plagiarists. However, similarity reports generated by most plagiarism detection systems provide a similarity score and produce matching sections of text with their possible sources. In this research we propose methods to identify two important paraphrase types – synonymous substitution and word reordering in paraphrased, plagiarised sentence pairs. We propose a three staged approach that uses context matching and pretrained word embeddings for identifying synonymous substitution and word reordering. Our proposed approach indicates that the use of Smith Waterman Algorithm for Plagiarism Detection and ConceptNet Numberbatch pretrained word embeddings produces the best performance in terms of $$\hbox {F}_1$$ F 1 scores. This research can be used to complement similarity reports generated by currently available plagiarism detection systems by incorporating methods to identify paraphrase types for plagiarism detection.

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Syntax-based Improvements to Plagiarism Detectors and their Evaluations

Cited by 17 publications

References 11 publications

Mossad: Defeating Software Plagiarism Detection

Mossad: Defeating Software Plagiarism Detection

Educating Students about Programming Plagiarism and Collusion via Formative Feedback

Paraphrase type identification for plagiarism detection using contexts and word embeddings

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