Wang, H., Lenihan, P. and Wang, Z. orcid.org/0000-0001-6157-0662 (2025) Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization. In: CGO '25: Proceedings of the 23rd ACM/IEEE International Symposium on Code Generation and Optimization. CGO '25: 23rd ACM/IEEE International Symposium on Code Generation and Optimization, 01-05 Mar 2025, Las Vegas, NV, USA. ACM , New York, NY , pp. 31-46. ISBN 9798400712753
Abstract
Supervised machine learning techniques have shown promising results in code analysis and optimization problems. However, a learning-based solution can be brittle because minor changes in hardware or application workloads – such as facing a new CPU architecture or code pattern – may jeopardize decision accuracy, ultimately undermining model robustness. We introduce Prom, an open-source library to enhance the robustness and performance of predictive models against such changes during deployment. Prom achieves this by using statistical assessments to identify test samples prone to mispredictions and using feedback on these samples to improve a deployed model. We showcase Prom by applying it to 13 representative machine learning models across 5 code analysis and optimization tasks. Our extensive evaluation demonstrates that Prom can successfully identify an average of 96% (up to 100%) of mispredictions. By relabeling up to 5% of the Prom-identified samples through incremental learning, Prom can help a deployed model achieve a performance comparable to that attained during its model training phase.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. |
Keywords: | Model reliability, Statistical assessment, Machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Distributed Systems & Services |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/X037304/1 EPSRC (Engineering and Physical Sciences Research Council) EP/X018202/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Jan 2025 10:26 |
Last Modified: | 19 Mar 2025 13:12 |
Published Version: | https://dl.acm.org/doi/10.1145/3696443.3708959 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3696443.3708959 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221073 |
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