A Plagiarism Detection Technique for Java Program Using Bytecode Analysis

Ji, Jeong-Hoon; Woo, Gyun; Cho, Hwan-Gue

doi:10.1109/iccit.2008.267

Cited by 20 publications

(15 citation statements)

References 8 publications

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“…The first two works are focused on Java programming language whereas the other two are focused on .NET programming language. Ji et al approach [36] is quite similar with standard lexical-token-sequence approach except that it replaces token sequence with bytecode sequence (bytecode is a low-level representation of Java programming language). Their work is extended by Karnalim [3] by incorporating several additional mechanisms such as method inlining, recursive handling, instruction generalization, and instruction interpretation.…”

Section: Related Workmentioning

confidence: 99%

“…In general, there are four works which incorporates low-level code for detecting source code plagiarism: the work of Ji et al [36], Karnalim [3], Juričić [41], and Juričić et al [10]. The first two works are focused on Java programming language whereas the other two are focused on .NET programming language.…”

Section: Related Workmentioning

confidence: 99%

“…This pattern is typically popular since it is frequently incorporated on most publicly available plagiarism detection systems. It is also incorporated on many related research works about source code plagiarism detection such as Lim et al [34], Shah et al [35], Kustanto & Liem [9], Ji et al [36], Djuric & Gasevic [6], and Pawelczak [37]. Nevertheless, not all structure-based approaches rely on standard lexical token sequence.…”

Section: Related Workmentioning

confidence: 99%

“…In other words, all instructions from various methods and classes are merged into one CIL sequence. We do not apply semantic-preserving preprocessing which is suggested by Ji et al [36] and Karnalim [3] since it may require additional processing time. In addition, most semantic-preserving preprocessing phases rely on instruction-specific features and file structure.…”

Section: Net Cil Preprocessingmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Net Cil Preprocessingmentioning

confidence: 99%

See 2 more Smart Citations

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

Rabbani¹,

Karnalim²

2017

JIOS

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show abstract

“…Therefore, in order to diagnose the plagiarism problem and set a standard, there must be a preceding operation of understanding the various elements of musical content. Then an analytic technique that infers the level of similarity from the various characteristics of musical content elements is used [14][15][16][17].…”

Section: Methodsmentioning

confidence: 99%