Style Analysis for Source Code Plagiarism Detection — An Analysis of a Dataset of Student Coursework

Abstract

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An intelligent decision support system for software plagiarism detection in academia

“…To obtain the most suitable classification results, we used the RF algorithm for the identification of similar source codes. Previously, numerous studies 30,31 recommended that RF is an effective classification algorithm for source code plagiarism detection. RF algorithm integrates a considerable number of independent bagged decision trees, trained over randomly, and then uniformly allocated the subgroups of the data.…”

Detection of Programming Plagiarism in Computing Education: A Systematic Mapping Study

“…After passing from the first to the seventh stage, the SMS found 33 studies for results analysis in the last stage to answer the three research questions [Chan et al 2013, Choi et al 2013, Kim et al 2013, Tian et al 2013, Zhang and Liu 2013, Ajmal et al 2014, Baby et al 2014, Kikuchi et al 2014, Lazar and Banias 2014, Pohuba et al 2014, Zhang et al 2014, Acompora and Cosma 2015, Dutta 2015, Jhi et al 2015, Oprişa and Ignat 2015, Sharma et al 2015, Soh et al 2015, Tian et al 2015, Ming et al 2016, Strilețchi et al 2016, Agrawal and Sharma 2017, Jain et al 2017, Kargén and Shahmehri 2017, Luo et al 2017, Mirza et al 2017, Mišić et al 2017, Schneider et al 2017, Sudhamani and Rangarajan 2017, Karnalim 2018, Roopam and Singh 2018.…”

“…It not common to share a source code dataset used for testing, only in the research made by [Mirza et al 2017] adopted this type of resource for the evaluation process. The other works created their tests by developing plagiarism models implemented with the collaboration of students from computing courses.…”

Text mining in education

“…(Hirokawa & Flanagan, 2018) Chat Classification Evaluation In this article, the authors approach the problem of identifying characteristic differences and the classification of native languages from the perspective of 15 automatically predicted writing errors by online language learners. (Mirza, Joy, & Cosma, 2017) Document NLP Evaluation This paper focuses to identify whether a data set consisting of student programming assignments is rich enough to apply coding style metrics to detect similarities between code sequences. This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension.…”