Operationalizing Assurance Cases for Data Scientists: A Showcase of
  Concepts and Tooling in the Context of Test Data Quality for Machine Learning
Lisa Jöckel Michael Kläs Janek Groß Pascal Gerber Markus Scholz Jonathan Eberle Marc Teschner Daniel Seifert Richard Hawkins John Molloy Jens Ottnad
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.