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DC FieldValueLanguage
dc.contributorFaculty of Engineeringen_US
dc.creatorWong, Ying Kit-
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleTransformer approach on source code vulnerability detectionen_US
dcterms.abstractSoftware quality is a major concern in the software business. Security attacks continue to escalate, leading to application failures, financial losses, and erosion of confidence. Therefore, it is critical to allow developers to discover security flaws in source code before deployment. Several static analysis techniques have been created to uncover security issues in source code. However, traditional approaches have not had a leveraged machine learning mechanism to reduce operational effort and improve detection performance.en_US
dcterms.abstractVulnerability identification is a crucial task in security. With the development of technology, identifying vulnerable source code has become a hot topic in the industry in order to handle large code bases. Applying natural language processing (NLP) to source code and building models to complete the analysis and digestion of source code has become one of the valuable vulnerability identification studies. Detecting errors before they cause damage is the desire of The solution. Previous efforts have usually been unsuccessful and inefficient.en_US
dcterms.abstractThis paper contributes an enhanced approach to detecting errors using the Transformer method in deep learning. The method is validated against a vulnerability benchmark database. We employ NLP to process source code as a sequence of text in a specific domain and embed the text by paying attention to a highly relevant portion of the code to solve the error detection problem with a pre-trained and then fine-tuned model.en_US
dcterms.extent1 volume (unpaged) : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Doctorateen_US
dcterms.LCSHComputer software -- Quality controlen_US
dcterms.LCSHDebugging in computer scienceen_US
dcterms.LCSHComputer securityen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/12469