TY  - CONF
A1  - Gerber, Daniel
A1  - Meitz, Lukas
A1  - Rosenbauer, Lukas
A1  - Hähner, Jörg
A2  - Kaindl, Hermann
A2  - Mannion, Mike
A2  - Maciaszek, Leszek
T1  - Unsupervised anomaly detection in continuous integration pipelines
T2  - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE, April 28-29, 2024, in Angers, France
N2  - 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.
Y1  - 2024
UR  - https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/117136
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-1171366
SN  - 978-989-758-696-5
SN  - 2184-4895
SP  - 336
EP  - 343
PB  - SciTePress
CY  - Setúbal
ER  -