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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Veröffentlicht in:2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) S. 571 - 576
Hauptverfasser: Pajic, Enil, Ljubovic, Vedran
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Croatian Society MIPRO 01.05.2019
Schlagworte:
Codes
Feature extraction
Genetic algorithms
Microelectronics
Plagiarism
plagiarism detection
Production
real-coded genetic algorithm
Software measurement
Source coding
Statistical analysis
Vectors
ISSN:2623-8764
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Beschreibung
Zusammenfassung: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.
ISSN:2623-8764
DOI:10.23919/MIPRO.2019.8756744