Triage Software Update Impact via Release Notes Classification Article Swipe
Solomon Berhe
,
Vladimir Kan
,
Omhier Khan
,
Nathan Pader
,
A.A. Farooqui
,
Marc Maynard
,
Foutse Khomh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2024.06.069
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2024.06.069
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.
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- Language
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- OA Status
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Triage Software Update Impact via Release Notes ClassificationWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-01-01Full publication date if available
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Solomon Berhe, Vladimir Kan, Omhier Khan, Nathan Pader, A.A. Farooqui, Marc Maynard, Foutse KhomhList of authors in order
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https://doi.org/10.1016/j.procs.2024.06.069Publisher landing page
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https://doi.org/10.1016/j.procs.2024.06.069Direct OA link when available
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5025683409 |
| countries_distinct_count | 2 |
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| corresponding_institution_ids | https://openalex.org/I119942576 |
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| citation_normalized_percentile.is_in_top_10_percent | False |