Fast Plagiarism Detection System

Mozgovoy, Maxim; Fredriksson, Kimmo; White, Daniel; Joy, Mike; Sutinen, Erkki

doi:10.1007/11575832_30

Cited by 39 publications

(33 citation statements)

References 5 publications

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“…Several of the plagiarism detection systems obtained O (n 2 ) class based on JPlag (Prechelt et al, 2002;Mozgovoy et al, 2005). Where n is the an input size (number of documents) of the dataset, and f(n) is the comparison time between one pair of documents of size n. A sample of comparison between the proposed method and other plagiarism detection method in term of the time complexity shown in Table 7.…”

Section: Resultsmentioning

confidence: 99%

SVM significant role selection method for improving semantic text plagiarism detection

Osman¹,

Barukab

2017

Int. j. adv. appl. sci.

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This research introduces an approach for the prediction and detection of plagiarized text based on Semantic Role Labelling (SRL) and Support Vector Machine (SVM). The introduced method evaluates and analyses text based on semantic position for each term within the text. It additionally detects the source semantic sense in considering the connections between its terms using the Semantic Role Labeling (SRL). SRL presents noteworthy remuneration while creating roles from a text semantically. Selecting for every role created by the SVM method keeping in mind the end goal to foresee significant roles is a noteworthy part of the proposed system. The imperative roles that will vote by the SVM strategy will be chosen in the comparability computation process. The proposed strategy assessed utilizing the PAN-PC-10 dataset. The outcomes proved that the introduced strategy enhanced the execution as far as the assessment measures contrasted and other plagiarism detection methods.

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Section: Resultsmentioning

confidence: 99%

SVM significant role selection method for improving semantic text plagiarism detection

Osman¹,

Barukab

2017

Int. j. adv. appl. sci.

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“…It is relatively robust to various plagiarism attacks since this approach is typically focused on the ordinal structure of the given source code. This finding is also strengthened by the fact that structurebased approach is frequently used on publicly available plagiarism detection systems such as JPlag [8], MOSS [27], Sim [28], Plaque [29], YAP [30], Plaggie [31], FPDS [32], and Marble [33]. In general, most structure-based approaches detect plagiarism by converting source codes into lexical token sequences and compare them based on a particular similarity algorithm.…”

Section: Related Workmentioning

confidence: 99%

Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment

Rabbani¹,

Karnalim²

2017

JIOS

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Even though there are various source code plagiarism detection approaches, only a few works which are focused on low-level representation for deducting similarity. Most of them are only focused on lexical token sequence extracted from source code. In our point of view, low-level representation is more beneficial than lexical token since its form is more compact than the source code itself. It only considers semantic-preserving instructions and ignores many source code delimiter tokens. This paper proposes a source code plagiarism detection which rely on low-level representation. For a case study, we focus our work on .NET programming languages with Common Intermediate Language as its low-level representation. In addition, we also incorporate Adaptive Local Alignment for detecting similarity. According to Lim et al, this algorithm outperforms code similarity state-of-the-art algorithm (i.e. Greedy String Tiling) in term of effectiveness. According to our evaluation which involves various plagiarism attacks, our approach is more effective and efficient when compared with standard lexical-token approach.

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“…The total dataset comprised 224 students and a total of 1420 different submissions. Regarding the implemented plagiarism detection tools, our current prototype supports JPlag (JP) [14], Sherlock (SH) [15], and SIM [16]. These tools were selected because they are well-known and widely used in the literature.…”

Section: A Experimental Setupmentioning

confidence: 99%

A unified approach to automate the usage of plagiarism detection tools in programming courses

Portillo-Dominguez

Ayala-Rivera

Murphy

et al. 2017

2017 12th International Conference on Computer Science and Education (ICCSE)

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Fast Plagiarism Detection System

Cited by 39 publications

References 5 publications

SVM significant role selection method for improving semantic text plagiarism detection

SVM significant role selection method for improving semantic text plagiarism detection

Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment

A unified approach to automate the usage of plagiarism detection tools in programming courses

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