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Triage Software Update Impact via Release Notes Classification.

Authors :
Berhe, Solomon
Kan, Vanessa
Khan, Omhier
Pader, Nathan
Farooqui, Ali Zain
Maynard, Marc
Khomh, Foutse
Source :
Procedia Computer Science; 2024, Vol. 238, p618-622, 5p
Publication Year :
2024

Abstract

In the rapidly evolving domain of Industry 4.0, effective management of software updates is crucial for maintaining system continuity and security. This paper presents a novel machine learning-based approach for a prompt and effective triage of software updates, leveraging an evaluation of six release note classifiers to categorize updates by component type, release type, and security risk. Our methodology, tested on a dataset of 1,000 release notes commonly encountered in Industry 4.0 ecosystems, demonstrates Logistic Regression as the most accurate classifier. The findings not only highlight the practical applicability of our approach in real-world data but also set the foundation for future enhancements to streamline the machine learning triage process further. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18770509
Volume :
238
Database :
Supplemental Index
Journal :
Procedia Computer Science
Publication Type :
Academic Journal
Accession number :
178317998
Full Text :
https://doi.org/10.1016/j.procs.2024.06.069