Program plagiarism detection with dynamic structure

Kuo, Jong-Yih; Cheng, Hsuan-Kuei; Wang, Ping-Feng

doi:10.1109/isne.2018.8394758

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Compared to other detection techniques which consider non-semantic-preserving information [11], [33]- [36], our technique is argued to be more sensitive to such information as its coverage is not limited to comments and whitespaces [11], [33-[35] and it relies on many features (83 features in total while the highest number of features used in existing techniques is only 17 [36]).…”

Section: Methodsmentioning

confidence: 99%

“…Comment similarity was also used in [33] in combination with variable type and content similarity. Unique to this, each similarity factor is determined with a specific measurement: comment similarity relies on a string matching algorithm, variable type similarity relies on Cosine Correlation, and content similarity relies on program dependency graph comparison.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Programming Style on Source Code Plagiarism and Collusion Detection

Karnalim¹,

Kurniawati²

2020

IJC

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“…Techniques proposed by Engels, Lakshmanan, & Craig (2007) and Poon et al (2012) utilise the result of a string-matching technique (structure-based technique) as a feature for an attributebased technique -which are learning-and clustering-based techniques respectively. A technique proposed by Kuo, Cheng, & Wang (2018) measures the similarity from three aspects (variable, comment, and function) using Cosine correlation; in which the IDE-generated comments are excluded from comparison via a string-matching technique.…”

Section: A Review Of Automated Source Code Plagiarism Detectionmentioning

confidence: 99%

Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

Karnalim¹,

Budi²,

Toba³

et al. 2019

Informatics in Education

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Source code plagiarism is an emerging issue in computer science education. As a result, a number of techniques have been proposed to handle this issue. However, comparing these techniques may be challenging, since they are evaluated with their own private dataset(s). This paper contributes in providing a public dataset for comparing these techniques. Specifically, the dataset is designed for evaluation with an Information Retrieval (IR) perspective. The dataset consists of 467 source code files, covering seven introductory programming assessment tasks. Unique to this dataset, both intention to plagiarise and advanced plagiarism attacks are considered in its construction. The dataset's characteristics were observed by comparing three IR-based detection techniques, and it is clear that most IR-based techniques are less effective than a baseline technique which relies on Running-Karp-Rabin Greedy-String-Tiling, even though some of them are far more time-efficient.

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“…Due to this ethical problem that affects several fields (Chuda et al 2012, Cheers & Lin 2022) besides the software industry, plagiarism detection approaches are proposed. The computer science contributes for the elaboration of these solutions (Allyson et al 2019, Kuo et al 2018, Meuschke et al 2018, Prechelt et al 2002, Cheers & Lin 2022 as well as other interdisciplinary fields, for example, the bioinformatics (Kane & Springer 2007, Pedersen et al 2012, Revett 2009, Xu et al 2006.…”

Section: Introductionmentioning

confidence: 99%

BIOPLAG: An Approach to Detect Programming Plagiarism

GOMES,

MATOS,

BINI

2023

An. Acad. Bras. Ciênc.

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This paper creates an approach to the automatic detection of plagiarism in programming by combining the interdisciplinary knowledge from bioinformatics with techniques such as: tokens of programming language elements, tokens mapping in synthetic biological sequence, and alignment of biological sequences. This solution, named BIOPLAG, contemplates different levels of modifications in source code, and its functioning depends on concepts of computer science as well. Through the realization of three experiments with students and programmers, a total of 168 real examples of plagiarism evaluated the implementation of BIOPLAG with the usage of 336 source codes in C language divided into seven scenarios. The evaluation process compared to other tools known as references in the literature: MOSS and JPLAG. The evaluative metrics used are precision, recall, and fmeasure. As a result, BIOPLAG showed to be the best in four and equal in three out of seven test scenarios. Its average score on the metrics in the evaluation process was 0.95 against MOSS with 0.83 and JPAG with 0.87.

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Program plagiarism detection with dynamic structure

Cited by 5 publications

References 4 publications

Programming Style on Source Code Plagiarism and Collusion Detection

Programming Style on Source Code Plagiarism and Collusion Detection

Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

BIOPLAG: An Approach to Detect Programming Plagiarism

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