| 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 | 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 | 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 |
| 2019 | SAFECOMP | Uncertainty Wrappers for Data-Driven Models - Increase the Transparency of AI/ML-Based Models Through Enrichment with Dependable Situation-Aware Uncertainty Estimates. | Michael Kls, Lena Sembach |
| 2018 | SAFECOMP | Uncertainty in Machine Learning Applications: A Practice-Driven Classification of Uncertainty. | Michael Kls, Anna Maria Vollmer |
| 2017 | PROFES | Managing Development Using Active Data Collection. | Michael Kls, Frank Elberzhager |
| 2015 | ICSE | A Large-Scale Technology Evaluation Study: Effects of Model-based Analysis and Testing. | Michael Kls, Thomas Bauer, Andreas Dereani, Thomas Soderqvist, Philipp Helle |
| 2013 | PROFES | Beyond Herding Cats: Aligning Quantitative Technology Evaluation in Large-Scale Research Projects. | Michael Kls, Thomas Bauer, Ubaldo Tiberi |
| 2012 | ICSE | The Quamoco product quality modelling and assessment approach. | Stefan Wagner, Klaus Lochmann, Lars Heinemann, Michael Kls, Adam Trendowicz, Reinhold Plsch, Andreas Seidl, Andreas Goeb, Jonathan Streit |
| 2012 | ISSRE | A Comprehensive Code-Based Quality Model for Embedded Systems: Systematic Development and Validation by Industrial Projects. | Alois Mayr, Reinhold Plsch, Michael Kls, Constanza Lampasona, Matthias Saft |
| 2011 | ESEM | Handling Estimation Uncertainty with Bootstrapping: Empirical Evaluation in the Context of Hybrid Prediction Methods. | Michael Kls, Adam Trendowicz, Yasushi Ishigai, Haruka Nakao |
| 2010 | ICSE | Transparent combination of expert and measurement data for defect prediction: an industrial case study. | Michael Kls, Frank Elberzhager, Jrgen Mnch, Klaus Hartjes, Olaf von Graevemeyer |
| 2008 | ESEM | Managing software quality through a hybrid defect content and effectiveness model. | Michael Kls, Frank Elberzhager, Haruka Nakao |
| 2008 | ISSRE | Predicting Defect Content and Quality Assurance Effectiveness by Combining Expert Judgment and Defect Data - A Case Study. | Michael Kls, Haruka Nakao, Frank Elberzhager, Jrgen Mnch |