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Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts.

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Uncertainty Wrappers for Data-Driven Models - Increase the Transparency of AI/ML-Based Models Through Enrichment with Dependable Situation-Aware Uncertainty Estimates., and . SAFECOMP Workshops, volume 11699 of Lecture Notes in Computer Science, page 358-364. Springer, (2019)Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts., , , , and . CoRR, (2021)Handling Uncertainty in Collaborative Embedded Systems Engineering., , , , , and . Model-Based Engineering of Collaborative Embedded Systems, Springer, (2021)Handling Uncertainties of Data-Driven Models in Compliance with Safety Constraints for Autonomous Behaviour., , , , and . EDCC, page 95-102. IEEE, (2021)Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice., , , , and . CoRR, (2023)From Complexity Measurement to Holistic Quality Evaluation for Automotive Software Development., , , , , , and . CoRR, (2021)Operationalised product quality models and assessment: The Quamoco approach., , , , , , , , , and 1 other author(s). Inf. Softw. Technol., (2015)A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models., , and . SafeAI@AAAI, volume 3087 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning., , , , , , , , , and 1 other author(s). PROFES (1), volume 14483 of Lecture Notes in Computer Science, page 151-158. Springer, (2023)Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview.. CoRR, (2018)