Source Code Plagiarism Detection Based on Abstract Syntax Tree Fingerprintings

dc.contributor.authorSuttichaya V.
dc.contributor.authorEakvorachai N.
dc.contributor.authorLurkraisit T.
dc.contributor.otherMahidol University
dc.date.accessioned2023-06-18T17:02:44Z
dc.date.available2023-06-18T17:02:44Z
dc.date.issued2022-01-01
dc.description.abstractSyntax 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.
dc.identifier.citationInternational 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)
dc.identifier.doi10.1109/iSAI-NLP56921.2022.9960266
dc.identifier.scopus2-s2.0-85143968395
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/84326
dc.rights.holderSCOPUS
dc.subjectComputer Science
dc.titleSource Code Plagiarism Detection Based on Abstract Syntax Tree Fingerprintings
dc.typeConference Paper
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85143968395&origin=inward
oaire.citation.titleInternational 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
oairecerif.author.affiliationMahidol University

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