Source Code Plagiarism Detection Based on Abstract Syntax Tree Fingerprintings
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85143968395
Journal Title
International Joint Conference 2022 - 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2022 and 3rd International Conference on Artificial Intelligence and Internet of Things, AIoT 2022
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SCOPUS
Bibliographic Citation
International Joint Conference 2022 - 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2022 and 3rd International Conference on Artificial Intelligence and Internet of Things, AIoT 2022 (2022)
Suggested Citation
Suttichaya V., Eakvorachai N., Lurkraisit T. Source Code Plagiarism Detection Based on Abstract Syntax Tree Fingerprintings. International Joint Conference 2022 - 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2022 and 3rd International Conference on Artificial Intelligence and Internet of Things, AIoT 2022 (2022). doi:10.1109/iSAI-NLP56921.2022.9960266 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84326
Title
Source Code Plagiarism Detection Based on Abstract Syntax Tree Fingerprintings
Author(s)
Author's Affiliation
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Abstract
Syntax Tree (AST) is an abstract logical structure of source code represented as a tree. This research utilizes information of fingerprinting with AST to locate the similarities between source codes. The proposed method can detect plagiarism in source codes using the number of duplicated logical structures. The structural information of program is stored in the fingerprints format. Then, the fingerprints of source codes are compared to identify number of similar nodes. The final output is calculated from number of similar nodes known as similarities scores. The result shows that the proposed method accurately captures the common modification techniques from basic to advance.