Jockel, Lisa, Klas, Michael, Gross, Janek et al. (2 more authors) (2023) Operationalizing Assurance Cases for Data Scientists:A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. In: Kadgien, R., Jedlitschka, A., Janes, A., Lenarduzzi, V. and Li, X., (eds.) International Conference on Product-Focused Software Process Improvement:Proceedings. Product-Focused Software Process Improvement, 10-13 Dec 2023 Lecture Notes in Computer Science . Springer , AUT , pp. 151-158.
Abstract
Assurance Cases (ACs) are an established approach in safety engineering to argue quality claims in a structured way. In the context of quality assurance for Machine Learning (ML)-based software components, ACs are also being discussed and appear promising. Tools for operationalizing ACs do exist, yet mainly focus on supporting safety engineers on the system level. However, assuring the quality of an ML component within the system is commonly the responsibility of data scientists, who are usually less familiar with these tools. To address this gap, we propose a framework to support the operationalization of ACs for ML components based on technologies that data scientists use on a daily basis: Python and Jupyter Notebook. Our aim is to make the process of creating ML-related evidence in ACs more effective. Results from the application of the framework, documented through notebooks, can be integrated into existing AC tools. We illustrate the application of the framework on an example excerpt concerned with the quality of the test data.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 07 Dec 2023 15:00 |
Last Modified: | 18 Dec 2024 00:41 |
Published Version: | https://doi.org/10.1007/978-3-031-49266-2_10 |
Status: | Published online |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-031-49266-2_10 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206364 |
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