Utilization of Machine Learning for the Detection of Self-admitted Vulnerabilities

Moritz Mock

doi:10.1007/978-3-031-49269-3_15

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Utilization of Machine Learning for the Detection of Self-admitted Vulnerabilities

Conference proceeding

Peer reviewed

Utilization of Machine Learning for the Detection of Self-admitted Vulnerabilities

Moritz Mock

Product-Focused Software Process Improvement: PROFES 2023, Vol.14484, pp.139-145

14484

24th International Conference on Product-Focused Software Process Improvement (PROFES 2023) (Dornbirn, 10/12/2023 - 13/12/2023)

2023

DOI: https://doi.org/10.1007/978-3-031-49269-3_15

Handle:

https://hdl.handle.net/10863/40991

Abstract

Code Comments

NL-PL

Self-Admitted Technical Debt

Software Vulnerabilities

Machine Learning

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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Details

Title: Utilization of Machine Learning for the Detection of Self-admitted Vulnerabilities
Creators: Moritz Mock - Free University of Bozen-Bolzano
Publication Details: Product-Focused Software Process Improvement: PROFES 2023, Vol.14484, pp.139-145
Editor(s): Kadgien R, Jedlitschka A, Janes A, Lenarduzzi V, Li X
ISBN: 9783031492686
EISBN: 9783031492693
Conference: 24th International Conference on Product-Focused Software Process Improvement (PROFES 2023) (Dornbirn, 10/12/2023 - 13/12/2023)
Series / Volume: 14484
Publisher: Springer
Number of pages: 7
Identifiers: 978-303149268-6
(UNIBZ)70062823
991006773996601241
Web of Science ID: WOS:001157575800015
Scopus ID: 2-s2.0-85185563909
Academic Unit: Faculty of Engineering
Language: English
Resource Type: Conference proceeding
Author Names String: Mock M
Additional Description: Editors/Supervisors: Kadgien R, Jedlitschka A, Janes A, Lenarduzzi V, Li X

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