A Search of Verilog Code Plagiarism Detection Method

Wang, Lisheng; Jiang, Lingchao; Qin, Guofeng

doi:10.1109/iccse.2018.8468817

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…RKRGST converts the student programs to token strings and then measure the similarities via running Karp-Rabin greedy string tiling [25], a common string-matching algorithm for code similarity detection techniques [32]. Structure works similarly but the token strings are the result of linearising the syntax trees in a pre-order manner, inspired from two former studies [26], [27]. These tokens are expected to be more resistant to surface modification as most of them cannot be modified directly at source code level.…”

Section: Methodsmentioning

confidence: 99%

A Scalable Code Similarity Detection with Online Architecture and Focused Comparison for Maintaining Academic Integrity in Programming

2020

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<p>Many code similarity detection techniques have been developed to maintain academic integrity in programming. However, most of them assume that the student programs are locally available, and the computation can be run on any computer specification. Further, their comparison in raising suspicion is time-consuming as the student programs are pairwise compared to one another. This paper proposes a scalable code similarity detection with online architecture and focused comparison. The former enables student programs shared among lecturers and guarantees that the computation is runnable. The latter shortens the execution time as only some students are considered, with inclusion criteria determined by the lecturers. To boost up the scalability, the similarity algorithm is cosine correlation, which computation is linear time. Our evaluation shows that focused comparison leads to fewer comparisons and cosine correlation leads to shorter execution time.</p>

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Section: Methodsmentioning

confidence: 99%

A Scalable Code Similarity Detection with Online Architecture and Focused Comparison for Maintaining Academic Integrity in Programming

2020

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“…Instead of applying heuristics in comparing abstract syntax tree, a technique in [31] converts the syntax trees to token strings through pre-order visit and therefore compares them with a string matching algorithm. This was followed by [32] but with hashing mechanism involved.…”

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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“…These techniques rely on various structures for comparison. Some of the structures are source code token strings [8,29,41], abstract syntax trees [20,30,53], parse trees [48], program dependency graphs [32], and low-level token strings [25,42].…”

Section: Related Workmentioning

confidence: 99%

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Karnalim

2020

csci

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Several computing courses allow students to choose which programming language they want to use for completing a programming task. This can lead to cross-language code plagiarism and collusion, in which the copied code file is rewritten in another programming language. In response to that, this paper proposes a detection technique which is able to accurately compare code files written in various programming languages, but with limited effort in accommodating such languages at development stage. The only language-dependent feature used in the technique is source code tokeniser and no code conversion is applied. The impact of coincidental similarity is reduced by applying a TF-IDF inspired weighting, in which rare matches are prioritised. Our evaluation shows that the technique outperforms common techniques in academia for handling language conversion disguises. Further, it is comparable to those techniques when dealing with conventional disguises.

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A Search of Verilog Code Plagiarism Detection Method

Cited by 8 publications

References 4 publications

A Scalable Code Similarity Detection with Online Architecture and Focused Comparison for Maintaining Academic Integrity in Programming

A Scalable Code Similarity Detection with Online Architecture and Focused Comparison for Maintaining Academic Integrity in Programming

Programming Style on Source Code Plagiarism and Collusion Detection

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

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