| Construction of a quality model for machine learning systems J Siebert, L Joeckel, J Heidrich, A Trendowicz, K Nakamichi, K Ohashi, ... Software Quality Journal 30 (2), 307-335, 2022 | 53 | 2022 |
| Towards guidelines for assessing qualities of machine learning systems J Siebert, L Joeckel, J Heidrich, K Nakamichi, K Ohashi, I Namba, ... Quality of Information and Communications Technology: 13th International …, 2020 | 53 | 2020 |
| Requirements-driven method to determine quality characteristics and measurements for machine learning software and its evaluation K Nakamichi, K Ohashi, I Namba, R Yamamoto, M Aoyama, L Joeckel, ... 2020 IEEE 28th International Requirements Engineering Conference (RE), 260-270, 2020 | 38 | 2020 |
| A framework for building uncertainty wrappers for AI/ML-based data-driven components M Kläs, L Jöckel Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops: DECSoS …, 2020 | 24 | 2020 |
| Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software L Jöckel, M Kläs, S Martínez-Fernández | 22 | 2019 |
| Increasing Trust in Data-Driven Model Validation - A Framework for Probabilistic Augmentation of Images and Meta-Data Generation using Application Scope Characteristics L Jöckel, M Kläs International Conference on Computer Safety, Reliability, and Security, 155-164, 2019 | 14 | 2019 |
| Using Complementary Risk Acceptance Criteria to Structure Assurance Cases for Safety-Critical AI Components. M Kläs, R Adler, L Jöckel, J Groß, J Reich AISafety@ IJCAI, 2021 | 13 | 2021 |
| CFD simulation and visualization of reactive bubble columns MW Hlawitschka, J Schaefer, L Joeckel, M Hummel, C Garth, HJ Bart Journal of Chemical Engineering of Japan 51 (4), 356-365, 2018 | 13 | 2018 |
| Handling Uncertainties of Data-Driven Models in Compliance with Safety Constraints for Autonomous Behaviour M Kläs, L Joeckel, R Adler, J Reich, I Sorokos | 12 | 2021 |
| Could We Relieve AI/ML Models of the Responsibility of Providing Dependable Uncertainty Estimates? A Study on Outside-Model Uncertainty Estimates L Jöckel, M Kläs | 7 | 2021 |
| Handling uncertainty in collaborative embedded systems engineering T Bandyszak, L Jöckel, M Kläs, S Törsleff, T Weyer, B Wirtz Model-Based Engineering of Collaborative Embedded Systems: Extensions of the …, 2021 | 7 | 2021 |
| Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception J Groß, R Adler, M Kläs, J Reich, L Jöckel, R Gansch International Conference on Computer Safety, Reliability, and Security, 284-297, 2022 | 6 | 2022 |
| A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models P Gerber, L Jöckel, M Kläs arXiv preprint arXiv:2201.03263, 2022 | 6 | 2022 |
| Towards a Common Testing Terminology for Software Engineering and Data Science Experts L Jöckel, T Bauer, M Kläs, MP Hauer, J Groß Product-Focused Software Process Improvement: 22nd International Conference …, 2021 | 6 | 2021 |
| Hardening of Artificial Neural Networks for Use in Safety-Critical Applications--A Mapping Study R Adler, MN Akram, P Bauer, P Feth, P Gerber, A Jedlitschka, L Jöckel, ... arXiv preprint arXiv:1909.03036, 2019 | 6 | 2019 |
| Visualizing Probabilistic Multi‐Phase Fluid Simulation Data using a Sampling Approach M Hummel, L Jöckel, J Schäfer, MW Hlawitschka, C Garth Computer Graphics Forum 36 (3), 469-477, 2017 | 4 | 2017 |
| Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning? L Jöckel, M Kläs, J Groß, P Gerber International Conference on Computer Safety, Reliability, and Security, 314-327, 2023 | 2 | 2023 |
| Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning J Groß, M Kläs, L Jöckel, P Gerber 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems …, 2023 | 2 | 2023 |
| Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components M Kläs, L Jöckel, R Adler, J Reich arXiv preprint arXiv:2202.05313, 2022 | 1 | 2022 |
| Evaluating Sampling Strategies for Visualizing Uncertain Multi-Phase Fluid Simulation Data M Hummel, L Jöckel, J Schäfer, MW Hlawitschka, C Garth Applied Mechanics and Materials 869, 139-148, 2017 | 1 | 2017 |
