Managing software quality through a hybrid defect content and effectiveness model. - Michael Kläs; Frank Elberzhager; Haruka Nakao (ESEM, 2008)

Predicting Defect Content and Quality Assurance Effectiveness by Combining Expert Judgment and Defect Data - A Case Study. - Michael Kläs; Haruka Nakao; Frank Elberzhager; Jürgen Münch (ISSRE, 2008)

The Use of Simulation Techniques for Hybrid Software Cost Estimation and Risk Analysis. - Michael Kläs; Adam Trendowicz; Axel Wickenkamp; Jürgen Münch; Nahomi Kikuchi; Yasushi Ishigai (Adv. Comput., 2008)

Quality models in practice: A preliminary analysis. - Stefan Wagner 001; Klaus Lochmann; Sebastian Winter; Andreas Goeb; Michael Klaes (ESEM, 2009)

A Framework for the Balanced Optimization of Quality Assurance Strategies Focusing on Small and Medium Sized Enterprises. - Michael Kläs; Frank Elberzhager; Rolf Hendrik van Lengen; Torsten Schulz; Jürgen Goebbels (EUROMICRO-SEAA, 2009)

CQML Scheme: A Classification Scheme for Comprehensive Quality Model Landscapes. - Michael Kläs; Jens Heidrich; Jürgen Münch; Adam Trendowicz (EUROMICRO-SEAA, 2009)

Transparent combination of expert and measurement data for defect prediction: an industrial case study. - Michael Kläs; Frank Elberzhager; Jürgen Münch; Klaus Hartjes; Olaf von Graevemeyer (ICSE (2), 2010)

Support planning and controlling of early quality assurance by combining expert judgment and defect data - a case study. - Michael Kläs; Haruka Nakao; Frank Elberzhager; Jürgen Münch (Empir. Softw. Eng., 2010)

Softwarequalitätsmodelle - Praxisempfehlungen und Forschungsagenda. - Stefan Wagner 001; Manfred Broy; Florian Deißenböck; Michael Kläs; Peter Liggesmeyer; Jürgen Münch; Jonathan Streit (Inform. Spektrum, 2010)

Handling Estimation Uncertainty with Bootstrapping: Empirical Evaluation in the Context of Hybrid Prediction Methods. - Michael Kläs; Adam Trendowicz; Yasushi Ishigai; Haruka Nakao (ESEM, 2011)

Adapting Software Quality Models: Practical Challenges, Approach, and First Empirical Results. - Michael Kläs; Constanza Lampasona; Jürgen Münch (EUROMICRO-SEAA, 2011)

The Quamoco product quality modelling and assessment approach. - Stefan Wagner 001; Klaus Lochmann; Lars Heinemann; Michael Kläs; Adam Trendowicz; Reinhold Plösch; Andreas Seidl; Andreas Goeb; Jonathan Streit (ICSE, 2012)

A Comprehensive Code-Based Quality Model for Embedded Systems: Systematic Development and Validation by Industrial Projects. - Alois Mayr; Reinhold Plösch; Michael Kläs; Constanza Lampasona; Matthias Saft (ISSRE, 2012)

Beyond Herding Cats: Aligning Quantitative Technology Evaluation in Large-Scale Research Projects. - Michael Kläs; Thomas Bauer; Ubaldo Tiberi (PROFES, 2013)

Model-Based Quality Management of Software Development Projects. - Jens Heidrich; H. Dieter Rombach; Michael Kläs (Software Project Management in a Changing World, 2014)

Comprehensive Landscapes for Software-related Quality Models. - Michael Kläs; Jens Heidrich; Jürgen Münch; Adam Trendowicz (CoRR, 2014)

Model-based Product Quality Evaluation with Multi-Criteria Decision Analysis. - Adam Trendowicz; Michael Kläs; Constanza Lampasona; Jürgen Münch; Christian Körner; Matthias Saft (CoRR, 2014)

A Large-Scale Technology Evaluation Study: Effects of Model-based Analysis and Testing. - Michael Kläs; Thomas Bauer; Andreas Dereani; Thomas Soderqvist; Philipp Helle (ICSE (2), 2015)

Operationalised product quality models and assessment: The Quamoco approach. - Stefan Wagner 001; Andreas Goeb; Lars Heinemann; Michael Kläs; Constanza Lampasona; Klaus Lochmann; Alois Mayr; Reinhold Plösch; Andreas Seidl; Jonathan Streit; Adam Trendowicz (Inf. Softw. Technol., 2015)

Quality Evaluation for Big Data: A Scalable Assessment Approach and First Evaluation Results. - Michael Kläs; Wolfgang Putz; Tobias Lutz (IWSM-Mensura, 2016)

Effekte modellbasierter Test- und Analyseverfahren in Unternehmen - Ergebnisse einer großangelegten empirischen Evaluation mittels industrieller Fallstudien. - Michael Klaes (Software Engineering, 2016)

Managing Development Using Active Data Collection. - Michael Kläs; Frank Elberzhager (PROFES, 2017)

HyDEEP: transparent combination of measurement and expert data for defect predictions. - Michael Kläs (Fraunhofer Verlag, 2017)

Uncertainty in Machine Learning Applications: A Practice-Driven Classification of Uncertainty. - Michael Kläs; Anna Maria Vollmer (SAFECOMP Workshops, 2018)

Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview. - Michael Kläs (CoRR, 2018)

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)

Uncertainty Wrappers for Data-Driven Models - Increase the Transparency of AI/ML-Based Models Through Enrichment with Dependable Situation-Aware Uncertainty Estimates. - Michael Kläs; Lena Sembach (SAFECOMP Workshops, 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)

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)

From Complexity Measurement to Holistic Quality Evaluation for Automotive Software Development. - Jens Heidrich; Michael Kläs; Andreas Morgenstern; Pablo Oliveira Antonino; Adam Trendowicz; Jochen Quante; Thomas Grundler (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)

Assurance Cases as Foundation Stone for Auditing AI-Enabled and Autonomous Systems: Workshop Results and Political Recommendations for Action from the ExamAI Project. - Rasmus Adler; Michael Klaes (HCI (45), 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)

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)

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)

Badgers: generating data quality deficits with Python. - Julien Siebert; Daniel Seifert; Patricia Kelbert; Michael Kläs; Adam Trendowicz (CoRR, 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)