research
∙
07/10/2023
Badgers: generating data quality deficits with Python
Generating context specific data quality deficits is necessary to experi...
share
research
∙
05/24/2023
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning
As the use of Artificial Intelligence (AI) components in cyber-physical ...
research
∙
06/14/2022
Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception
Data-driven models (DDM) based on machine learning and other AI techniqu...
research
∙
02/10/2022
Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components
In the future, AI will increasingly find its way into systems that can p...
research
∙
01/10/2022
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models
Outcomes of data-driven AI models cannot be assumed to be always correct...
research
∙
10/27/2021
From Complexity Measurement to Holistic Quality Evaluation for Automotive Software Development
In recent years, the role and the importance of software in the automoti...
research
∙
08/31/2021
Towards a Common Testing Terminology for Software Engineering and Data Science Experts
Analytical quality assurance, especially testing, is an integral part of...
research
∙
09/02/2019
Hardening of Artificial Neural Networks for Use in Safety-Critical Applications – A Mapping Study
Context: Across different domains, Artificial Neural Networks (ANNs) are...
research
∙
11/28/2018