Unsupervised Anomaly Detection in Continuous Integration Pipelines

Daniel Gerber, Lukas Meitz, Lukas Rosenbauer, Jörg Hähner

2024

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.

Paper Citation

in Harvard Style

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 - Volume 1: ENASE; ISBN 978-989-758-696-5, SciTePress, pages 336-343. DOI: 10.5220/0012618500003687

in Bibtex Style

@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 - Volume 1: ENASE},
year={2024},
pages={336-343},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012618500003687},
isbn={978-989-758-696-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Unsupervised Anomaly Detection in Continuous Integration Pipelines
SN - 978-989-758-696-5
AU - Gerber D.
AU - Meitz L.
AU - Rosenbauer L.
AU - Hähner J.
PY - 2024
SP - 336
EP - 343
DO - 10.5220/0012618500003687
PB - SciTePress