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Authors: Daniel Gerber 1 ; Lukas Meitz 2 ; Lukas Rosenbauer 1 and Jörg Hähner 3

Affiliations: 1 BSH Hausgeräte GmbH, Im Gewerbepark B10, 93059 Regensburg, Germany ; 2 Technische Hochschule Augsburg, Fakultät Informatik, An der Hochschule 1, 86161 Augsburg, Germany ; 3 Universität Augsburg, Lehrstuhl für Organic Computing, Am Technologiezentrum 8, 86159 Augsburg, Germany

Keyword(s): Software Testing, Integration Testing, Performance Data, Machine Learning, Unsupervised Learning, Anomaly Detection.

Abstract: 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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Paper citation in several formats:
Gerber, D.; Meitz, L.; Rosenbauer, L. and Hähner, J. (2024). Unsupervised Anomaly Detection in Continuous Integration Pipelines. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-696-5; ISSN 2184-4895, SciTePress, pages 336-343. DOI: 10.5220/0012618500003687

@conference{enase24,
author={Daniel Gerber. and Lukas Meitz. and Lukas Rosenbauer. and Jörg Hähner.},
title={Unsupervised Anomaly Detection in Continuous Integration Pipelines},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2024},
pages={336-343},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012618500003687},
isbn={978-989-758-696-5},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Unsupervised Anomaly Detection in Continuous Integration Pipelines
SN - 978-989-758-696-5
IS - 2184-4895
AU - Gerber, D.
AU - Meitz, L.
AU - Rosenbauer, L.
AU - Hähner, J.
PY - 2024
SP - 336
EP - 343
DO - 10.5220/0012618500003687
PB - SciTePress

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