Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment
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 mo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Zagreb, Faculty of organization and informatics
2017-06-01
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| Series: | Journal of Information and Organizational Sciences |
| Subjects: | |
| Online Access: | https://jios.foi.hr/index.php/jios/article/view/1086 |
| _version_ | 1818882662434078720 |
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| author | Faqih Salban Rabbani Oscar Karnalim |
| author_facet | Faqih Salban Rabbani Oscar Karnalim |
| author_sort | Faqih Salban Rabbani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T15:21:19Z |
| format | Article |
| id | doaj.art-69137684c4154203b3a8332e11257de3 |
| institution | Directory Open Access Journal |
| issn | 1846-3312 1846-9418 |
| language | English |
| last_indexed | 2024-12-19T15:21:19Z |
| publishDate | 2017-06-01 |
| publisher | University of Zagreb, Faculty of organization and informatics |
| record_format | Article |
| series | Journal of Information and Organizational Sciences |
| spelling | doaj.art-69137684c4154203b3a8332e11257de32022-12-21T20:16:00ZengUniversity of Zagreb, Faculty of organization and informaticsJournal of Information and Organizational Sciences1846-33121846-94182017-06-0141110.31341/jios.41.1.71086Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local AlignmentFaqih Salban Rabbani0Oscar Karnalim1Maranatha Christian UniversityMaranatha Christian UniversityEven 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.https://jios.foi.hr/index.php/jios/article/view/1086source code plagiarism detectionsource code similaritylow-level language.NET programming languageadaptive local alignment |
| spellingShingle | Faqih Salban Rabbani Oscar Karnalim Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment Journal of Information and Organizational Sciences source code plagiarism detection source code similarity low-level language .NET programming language adaptive local alignment |
| title | Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment |
| title_full | Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment |
| title_fullStr | Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment |
| title_full_unstemmed | Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment |
| title_short | Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment |
| title_sort | detecting source code plagiarism on net programming languages using low level representation and adaptive local alignment |
| topic | source code plagiarism detection source code similarity low-level language .NET programming language adaptive local alignment |
| url | https://jios.foi.hr/index.php/jios/article/view/1086 |
| work_keys_str_mv | AT faqihsalbanrabbani detectingsourcecodeplagiarismonnetprogramminglanguagesusinglowlevelrepresentationandadaptivelocalalignment AT oscarkarnalim detectingsourcecodeplagiarismonnetprogramminglanguagesusinglowlevelrepresentationandadaptivelocalalignment |