Improving Plagiarism Detection Using Genetic Algorithm
Detecting instances of plagiarism in student home-work, with program code in particular, is a subject of active research for over 30 years. One of the early proposed methods was extraction and comparison of source-code metrics. Even though this approach has low algorithmic complexity, it is rarely u...
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| Vydané v: | 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) s. 571 - 576 |
|---|---|
| Hlavní autori: | , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
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Croatian Society MIPRO
01.05.2019
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| ISSN: | 2623-8764 |
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| Abstract | Detecting instances of plagiarism in student home-work, with program code in particular, is a subject of active research for over 30 years. One of the early proposed methods was extraction and comparison of source-code metrics. Even though this approach has low algorithmic complexity, it is rarely used in recent papers with some authors claiming that better results are obtained using other methods. In this paper, plagiarism detection is treated as an information retrieval problem, specifically query-by-example (QbE). A feature vector is constructed from source metrics and compared using common similarity measures. Further, evolutionary computation methods are used to optimize the similarity measure. It is shown that, by several metrics used, detection results are on par with state-of-the-art methods with significantly lower execution time. |
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| AbstractList | Detecting instances of plagiarism in student home-work, with program code in particular, is a subject of active research for over 30 years. One of the early proposed methods was extraction and comparison of source-code metrics. Even though this approach has low algorithmic complexity, it is rarely used in recent papers with some authors claiming that better results are obtained using other methods. In this paper, plagiarism detection is treated as an information retrieval problem, specifically query-by-example (QbE). A feature vector is constructed from source metrics and compared using common similarity measures. Further, evolutionary computation methods are used to optimize the similarity measure. It is shown that, by several metrics used, detection results are on par with state-of-the-art methods with significantly lower execution time. |
| Author | Pajic, Enil Ljubovic, Vedran |
| Author_xml | – sequence: 1 givenname: Enil surname: Pajic fullname: Pajic, Enil organization: Faculty of Electrical Engineering, University of Sarajevo, BiH – sequence: 2 givenname: Vedran surname: Ljubovic fullname: Ljubovic, Vedran organization: Faculty of Electrical Engineering, University of Sarajevo, BiH |
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| Snippet | Detecting instances of plagiarism in student home-work, with program code in particular, is a subject of active research for over 30 years. One of the early... |
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| SubjectTerms | Codes Feature extraction Genetic algorithms Microelectronics Plagiarism plagiarism detection Production real-coded genetic algorithm Software measurement Source coding Statistical analysis Vectors |
| Title | Improving Plagiarism Detection Using Genetic Algorithm |
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