Utilization of machine learning for the detection of self-admitted vulnerabilities

• URL: http://arxiv.org/abs/2309.15619v1
• Date: Wed, 27 Sep 2023 12:38:12 GMT
• Title: Utilization of machine learning for the detection of self-admitted vulnerabilities
• Authors: Moritz Mock
• Abstract summary: Technical debt is a metaphor that describes not-quite-right code introduced for short-term needs. Developers are aware of it and admit it in source code comments, which is called Self- Admitted Technical Debt (SATD)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Motivation: Technical debt is a metaphor that describes not-quite-right code introduced for short-term needs. Developers are aware of it and admit it in source code comments, which is called Self- Admitted Technical Debt (SATD). Therefore, SATD indicates weak code that developers are aware of. Problem statement: Inspecting source code is time-consuming; automatically inspecting source code for its vulnerabilities is a crucial aspect of developing software. It helps practitioners reduce the time-consuming process and focus on vulnerable aspects of the source code. Proposal: Accurately identify and better understand the semantics of self-admitted technical debt (SATD) by leveraging NLP and NL-PL approaches to detect vulnerabilities and the related SATD. Finally, a CI/CD pipeline will be proposed to make the vulnerability discovery process easily accessible to practitioners.

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