| 2024 | PROFES | Can Large Language Models (LLMs) Compete with Human Requirements Reviewers? - Replication of an Inspection Experiment on Requirements Documents. | Daniel Seifert, Lisa Jckel, Adam Trendowicz, Marcus Ciolkowski, Thorsten Honroth, Andreas Jedlitschka |
| 2023 | DSN | Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. | Janek Gro, Michael Kls, Lisa Jckel, Pascal Gerber |
| 2023 | PROFES | Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. | Lisa Jckel, Michael Kls, Janek Gro, Pascal Gerber, Markus Scholz, Jonathan Eberle, Marc Teschner, Daniel Seifert, Richard Hawkins, John Molloy, Jens Ottnad |
| 2023 | SAFECOMP | Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning? | Lisa Jckel, Michael Kls, Janek Gro, Pascal Gerber |
| 2022 | AAAI | A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. | Pascal Gerber, Lisa Jckel, Michael Kls |
| 2022 | SAFECOMP | Architectural Patterns for Handling Runtime Uncertainty of Data-Driven Models in Safety-Critical Perception. | Janek Gro, Rasmus Adler, Michael Kls, Jan Reich, Lisa Jckel, Roman Gansch |
| 2021 | EDCC | Handling Uncertainties of Data-Driven Models in Compliance with Safety Constraints for Autonomous Behaviour. | Michael Kls, Rasmus Adler, Ioannis Sorokos, Lisa Jckel, Jan Reich |
| 2021 | IJCAI | Using Complementary Risk Acceptance Criteria to Structure Assurance Cases for Safety-Critical AI Components. | Michael Klaes, Rasmus Adler, Lisa Jckel, Janek Gro, Jan Reich |
| 2021 | PROFES | Towards a Common Testing Terminology for Software Engineering and Data Science Experts. | Lisa Jckel, Thomas Bauer, Michael Kls, Marc P. Hauer, Janek Gro |
| 2021 | SAFECOMP | Could We Relieve AI/ML Models of the Responsibility of Providing Dependable Uncertainty Estimates? A Study on Outside-Model Uncertainty Estimates. | Lisa Jckel, Michael Kls |
| 2020 | QUATIC | Towards Guidelines for Assessing Qualities of Machine Learning Systems. | Julien Siebert, Lisa Jckel, Jens Heidrich, Koji Nakamichi, Kyoko Ohashi, Isao Namba, Rieko Yamamoto, Mikio Aoyama |
| 2020 | RE | 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 Jckel, Julien Siebert, Jens Heidrich |
| 2020 | SAFECOMP | A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components. | Michael Kls, Lisa Jckel |
| 2019 | QRS | Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software. | Lisa Jckel, Michael Kls, Silverio Martnez-Fernndez |
| 2019 | SAFECOMP | Increasing Trust in Data-Driven Model Validation - A Framework for Probabilistic Augmentation of Images and Meta-data Generation Using Application Scope Characteristics. | Lisa Jckel, Michael Kls |