Unsupervised anomaly detection in continuous integration pipelines

  • Modern embedded systems comprise more and more software. This yields novel challenges in development and quality assurance. Complex software interactions may lead to serious performance issues that can have a crucial economic impact if they are not resolved during development. Henceforth, we decided to develop and evaluate a machine learning-based approach to identify performance issues. Our experiments using real-world data show the applicability of our methodology and outline the value of an integration into modern software processes such as continuous integration.

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Metadaten
Author:Daniel Gerber, Lukas MeitzORCiD, Lukas Rosenbauer, Jörg HähnerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1171366
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/117136
ISBN:978-989-758-696-5OPAC
ISSN:2184-4895OPAC
Parent Title (English):Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE, April 28-29, 2024, in Angers, France
Publisher:SciTePress
Place of publication:Setúbal
Editor:Hermann Kaindl, Mike Mannion, Leszek Maciaszek
Type:Conference Proceeding
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2024/11/28
First Page:336
Last Page:343
DOI:https://doi.org/10.5220/0012618500003687
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)

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