SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities.

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Publication Details

Published
2024-10-17
DOI
https://doi.org/10.1109/icbds61829.2024.10837209
Primary Topic
Academic integrity and plagiarism
Type
article
Field-Weighted Citation Impact
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article

SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees

Nakul Sharma, Siddharth Shinde, Siddhi A. Bhosale, Sunil Patil
Academic integrity and plagiarism
article

SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees

Nakul Sharma, Siddharth Shinde, Siddhi A. Bhosale, Sunil Patil
article en

Abstract

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities.

International Institute of Information Technology (IN)
Openalex Percentile: Top 69%
Academic integrity and plagiarism
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