Visualizing Probabilistic Multi-Phase Fluid Simulation Data using a Sampling Approach. - Mathias Hummel; Lisa Jöckel; J. Schäfer; Mark W. Hlawitschka; Christoph Garth (Comput. Graph. Forum, 2017)

Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software. - Lisa Jöckel; Michael Kläs; Silverio Martínez-Fernández (QRS Companion, 2019)

Increasing Trust in Data-Driven Model Validation - A Framework for Probabilistic Augmentation of Images and Meta-data Generation Using Application Scope Characteristics. - Lisa Jöckel; Michael Kläs (SAFECOMP, 2019)

Hardening of Artificial Neural Networks for Use in Safety-Critical Applications - A Mapping Study. - Rasmus Adler; Mohammed Naveed Akram; Pascal Bauer; Patrik Feth; Pascal Gerber; Andreas Jedlitschka; Lisa Jöckel; Michael Kläs; Daniel Schneider 001 (CoRR, 2019)

Towards Guidelines for Assessing Qualities of Machine Learning Systems. - Julien Siebert; Lisa Jöckel; Jens Heidrich; Koji Nakamichi; Kyoko Ohashi; Isao Namba; Rieko Yamamoto; Mikio Aoyama (QUATIC, 2020)

Requirements-Driven Method to Determine Quality Characteristics and Measurements for Machine Learning Software and Its Evaluation. - Koji Nakamichi; Kyoko Ohashi; Isao Namba; Rieko Yamamoto; Mikio Aoyama; Lisa Jöckel; Julien Siebert; Jens Heidrich (RE, 2020)

A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components. - Michael Kläs; Lisa Jöckel (SAFECOMP Workshops, 2020)

Handling Uncertainty in Collaborative Embedded Systems Engineering. - Torsten Bandyszak; Lisa Jöckel; Michael Kläs; Sebastian Törsleff; Thorsten Weyer; Boris Wirtz (Model-Based Engineering of Collaborative Embedded Systems, 2021)

Handling Uncertainties of Data-Driven Models in Compliance with Safety Constraints for Autonomous Behaviour. - Michael Kläs; Rasmus Adler; Ioannis Sorokos; Lisa Jöckel; Jan Reich (EDCC, 2021)

Using Complementary Risk Acceptance Criteria to Structure Assurance Cases for Safety-Critical AI Components. - Michael Klaes; Rasmus Adler; Lisa Jöckel; Janek Groß; Jan Reich (AISafety@IJCAI, 2021)

Towards a Common Testing Terminology for Software Engineering and Data Science Experts. - Lisa Jöckel; Thomas Bauer; Michael Kläs; Marc P. Hauer; Janek Groß (PROFES, 2021)

Could We Relieve AI/ML Models of the Responsibility of Providing Dependable Uncertainty Estimates? A Study on Outside-Model Uncertainty Estimates. - Lisa Jöckel; Michael Kläs (SAFECOMP, 2021)

Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts. - Lisa Jöckel; Thomas Bauer; Michael Kläs; Marc P. Hauer; Janek Groß (CoRR, 2021)

A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. - Pascal Gerber; Lisa Jöckel; Michael Kläs (SafeAI@AAAI, 2022)

Architectural Patterns for Handling Runtime Uncertainty of Data-Driven Models in Safety-Critical Perception. - Janek Groß; Rasmus Adler; Michael Kläs; Jan Reich; Lisa Jöckel; Roman Gansch (SAFECOMP, 2022)

Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components. - Michael Kläs; Lisa Jöckel; Rasmus Adler; Jan Reich (CoRR, 2022)

Construction of a quality model for machine learning systems. - Julien Siebert; Lisa Jöckel; Jens Heidrich; Adam Trendowicz; Koji Nakamichi; Kyoko Ohashi; Isao Namba; Rieko Yamamoto; Mikio Aoyama (Softw. Qual. J., 2022)

Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. - Janek Groß; Michael Kläs; Lisa Jöckel; Pascal Gerber (DSN-W, 2023)

Reliability Estimation of ML for Image Perception: A Lightweight Nonlinear Transformation Approach Based on Full Reference Image Quality Metrics. - João-Vitor Zacchi; Francesco Carella; Priyank Upadhya; Shanza Ali Zafar; John Molloy; Lisa Jöckel; Janek Groß; Núria Mata; Nguyen Anh Vu Doan (MCSoC, 2023)

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 (PROFES (1), 2023)

Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning? - Lisa Jöckel; Michael Kläs; Janek Groß; Pascal Gerber (SAFECOMP Workshops, 2023)

Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice. - Lisa Jöckel; Michael Kläs; Georg Popp; Nadja Hilger; Stephan Fricke (CoRR, 2023)

Can Large Language Models (LLMs) Compete with Human Requirements Reviewers? - Replication of an Inspection Experiment on Requirements Documents. - Daniel Seifert; Lisa Jöckel; Adam Trendowicz; Marcus Ciolkowski; Thorsten Honroth; Andreas Jedlitschka (PROFES, 2024)