Improving the Reliability of Failure Prediction Models through Concept Drift Monitoring

Conference Paper (2025)
Author(s)

L. Poenaru-Olaru (TU Delft - Software Engineering)

L. Cruz (TU Delft - Software Engineering)

Jan Rellermeyer (TU Delft - Data-Intensive Systems, Leibniz Universität)

Arie Deursen (TU Delft - Software Engineering)

Research Group
Software Engineering
DOI related publication
https://doi.org/10.1109/DeepTest66595.2025.00006
More Info
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Publication Year
2025
Language
English
Research Group
Software Engineering
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository as part of the Taverne amendment. More information about this copyright law amendment can be found at https://www.openaccess.nl. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Pages (from-to)
1-8
ISBN (electronic)
9798331501907
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Abstract

Failure prediction models can be significantly beneficial for managing large-scale complex software systems, but their trustworthiness is severely affected by changes in the data over time, also known as concept drift. Thus, monitoring these models against concept drift and retraining them when the data changes becomes crucial in designing reliable failure prediction models. In this work, we evaluate the effects of monitoring failure prediction models over time using label-independent (unsupervised) drift detectors. We show that retraining based on unsupervised drift detectors instead of periodically reduces the cost of acquiring true labels without compromising accuracy. Furthermore, we propose a novel feature reduction for unsupervised drift detectors and an evaluation pipeline that practitioners can employ to select the most suitable unsupervised drift detector for their application.

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