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2020 – today
- 2023
[c30]Janek Groß, Michael Kläs, Lisa Jöckel, Pascal Gerber:
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. DSN-W 2023: 231-238- [c29]Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber, Markus Scholz, Jonathan Eberle, Marc Teschner, Daniel Seifert, Richard Hawkins, John Molloy
, Jens Ottnad:
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. PROFES (1) 2023: 151-158 - [c28]Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber:
Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning? SAFECOMP Workshops 2023: 314-327 - [i19]Janek Groß, Michael Kläs, Lisa Jöckel, Pascal Gerber:
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. CoRR abs/2305.14872 (2023) - [i18]Julien Siebert, Daniel Seifert, Patricia Kelbert, Michael Kläs, Adam Trendowicz:
Badgers: generating data quality deficits with Python. CoRR abs/2307.04468 (2023) - [i17]Lisa Jöckel, Michael Kläs, Georg Popp, Nadja Hilger, Stephan Fricke:
Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice. CoRR abs/2311.05245 (2023) - [i16]Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber, Markus Scholz, Jonathan Eberle, Marc Teschner, Daniel Seifert, Richard Hawkins, John Molloy, Jens Ottnad:
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. CoRR abs/2312.04917 (2023) - 2022
- [c27]Pascal Gerber, Lisa Jöckel, Michael Kläs:
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. SafeAI@AAAI 2022 - [c26]Rasmus Adler, Michael Klaes:
Assurance Cases as Foundation Stone for Auditing AI-Enabled and Autonomous Systems: Workshop Results and Political Recommendations for Action from the ExamAI Project. HCI (45) 2022: 283-300 - [c25]Janek Groß, Rasmus Adler, Michael Kläs, Jan Reich, Lisa Jöckel, Roman Gansch:
Architectural Patterns for Handling Runtime Uncertainty of Data-Driven Models in Safety-Critical Perception. SAFECOMP 2022: 284-297 - [i15]Pascal Gerber, Lisa Jöckel, Michael Kläs:
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. CoRR abs/2201.03263 (2022) - [i14]Michael Kläs, Lisa Jöckel, Rasmus Adler, Jan Reich:
Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components. CoRR abs/2202.05313 (2022) - [i13]Janek Groß, Rasmus Adler, Michael Kläs, Jan Reich, Lisa Jöckel, Roman Gansch:
Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception. CoRR abs/2206.06838 (2022) - [i12]Rasmus Adler, Michael Klaes:
Assurance Cases as Foundation Stone for Auditing AI-enabled and Autonomous Systems: Workshop Results and Political Recommendations for Action from the ExamAI Project. CoRR abs/2208.08198 (2022) - 2021
- [c24]Michael Kläs, Rasmus Adler, Ioannis Sorokos, Lisa Jöckel, Jan Reich:
Handling Uncertainties of Data-Driven Models in Compliance with Safety Constraints for Autonomous Behaviour. EDCC 2021: 95-102 - [c23]Michael Klaes, Rasmus Adler, Lisa Jöckel, Janek Groß, Jan Reich:
Using Complementary Risk Acceptance Criteria to Structure Assurance Cases for Safety-Critical AI Components. AISafety@IJCAI 2021 - [c22]Lisa Jöckel, Thomas Bauer, Michael Kläs, Marc P. Hauer, Janek Groß:
Towards a Common Testing Terminology for Software Engineering and Data Science Experts. PROFES 2021: 281-289 - [c21]Lisa Jöckel, Michael Kläs:
Could We Relieve AI/ML Models of the Responsibility of Providing Dependable Uncertainty Estimates? A Study on Outside-Model Uncertainty Estimates. SAFECOMP 2021: 18-33 - [p2]Torsten Bandyszak, Lisa Jöckel, Michael Kläs, Sebastian Törsleff, Thorsten Weyer, Boris Wirtz:
Handling Uncertainty in Collaborative Embedded Systems Engineering. Model-Based Engineering of Collaborative Embedded Systems 2021: 147-170 - [i11]Lisa Jöckel, Thomas Bauer, Michael Kläs, Marc P. Hauer, Janek Groß:
Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts. CoRR abs/2108.13837 (2021) - [i10]Jens Heidrich, Michael Kläs, Andreas Morgenstern, Pablo Oliveira Antonino, Adam Trendowicz, Jochen Quante, Thomas Grundler:
From Complexity Measurement to Holistic Quality Evaluation for Automotive Software Development. CoRR abs/2110.14301 (2021) - 2020
- [c20]Michael Kläs, Lisa Jöckel:
A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components. SAFECOMP Workshops 2020: 315-327
2010 – 2019
- 2019
- [c19]Lisa Jöckel, Michael Kläs, Silverio Martínez-Fernández:
Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software. QRS Companion 2019: 540-541 - [c18]Lisa Jöckel, Michael Kläs:
Increasing Trust in Data-Driven Model Validation - A Framework for Probabilistic Augmentation of Images and Meta-data Generation Using Application Scope Characteristics. SAFECOMP 2019: 155-164 - [c17]Michael Kläs, Lena Sembach:
Uncertainty Wrappers for Data-Driven Models - Increase the Transparency of AI/ML-Based Models Through Enrichment with Dependable Situation-Aware Uncertainty Estimates. SAFECOMP Workshops 2019: 358-364 - [i9]Rasmus Adler, Mohammed Naveed Akram, Pascal Bauer, Patrik Feth, Pascal Gerber, Andreas Jedlitschka, Lisa Jöckel, Michael Kläs, Daniel Schneider:
Hardening of Artificial Neural Networks for Use in Safety-Critical Applications - A Mapping Study. CoRR abs/1909.03036 (2019) - 2018
- [c16]Michael Kläs, Anna Maria Vollmer:
Uncertainty in Machine Learning Applications: A Practice-Driven Classification of Uncertainty. SAFECOMP Workshops 2018: 431-438 - [i8]Michael Kläs:
Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview. CoRR abs/1811.11669 (2018) - 2017
- [b1]Michael Kläs:
HyDEEP: transparent combination of measurement and expert data for defect predictions. Kaiserslautern University of Technology, Germany, Fraunhofer Verlag 2017, ISBN 978-3-8396-1149-4, pp. 1-273 - [c15]Michael Kläs, Frank Elberzhager:
Managing Development Using Active Data Collection. PROFES 2017: 543-547 - 2016
- [c14]Michael Kläs, Wolfgang Putz, Tobias Lutz:
Quality Evaluation for Big Data: A Scalable Assessment Approach and First Evaluation Results. IWSM-Mensura 2016: 115-124 - [c13]Michael Klaes:
Effekte modellbasierter Test- und Analyseverfahren in Unternehmen - Ergebnisse einer großangelegten empirischen Evaluation mittels industrieller Fallstudien. Software Engineering 2016: 65-66 - [i7]Stefan Wagner, Klaus Lochmann, Lars Heinemann, Michael Kläs, Adam Trendowicz, Reinhold Plösch, Andreas Seidl, Andreas Goeb, Jonathan Streit:
The Quamoco Product Quality Modelling and Assessment Approach. CoRR abs/1611.04433 (2016) - [i6]Stefan Wagner, Andreas Goeb, Lars Heinemann, Michael Kläs, Constanza Lampasona, Klaus Lochmann, Alois Mayr, Reinhold Plösch, Andreas Seidl, Jonathan Streit, Adam Trendowicz:
Operationalised product quality models and assessment: The Quamoco approach. CoRR abs/1611.09230 (2016) - 2015
- [j4]Stefan Wagner, Andreas Goeb, Lars Heinemann, Michael Kläs, Constanza Lampasona, Klaus Lochmann, Alois Mayr, Reinhold Plösch, Andreas Seidl, Jonathan Streit, Adam Trendowicz:
Operationalised product quality models and assessment: The Quamoco approach. Inf. Softw. Technol. 62: 101-123 (2015) - [c12]Michael Kläs, Thomas Bauer, Andreas Dereani, Thomas Soderqvist, Philipp Helle:
A Large-Scale Technology Evaluation Study: Effects of Model-based Analysis and Testing. ICSE (2) 2015: 119-128 - 2014
- [p1]Jens Heidrich, H. Dieter Rombach, Michael Kläs:
Model-Based Quality Management of Software Development Projects. Software Project Management in a Changing World 2014: 125-156 - [i5]Michael Kläs, Frank Elberzhager, Jürgen Münch, Klaus Hartjes, Olaf von Graevemeyer:
Transparent Combination of Expert and Measurement Data for Defect Prediction: An Industrial Case Study. CoRR abs/1401.1326 (2014) - [i4]Adam Trendowicz, Michael Kläs, Constanza Lampasona, Jürgen Münch, Christian Körner, Matthias Saft:
Model-based Product Quality Evaluation with Multi-Criteria Decision Analysis. CoRR abs/1401.1913 (2014) - [i3]Michael Kläs, Haruka Nakao, Frank Elberzhager, Jürgen Münch:
Predicting Defect Content and Quality Assurance Effectiveness by Combining Expert Judgment and Defect Data - A Case Study. CoRR abs/1401.2757 (2014) - [i2]Michael Kläs, Jens Heidrich, Jürgen Münch, Adam Trendowicz:
Comprehensive Landscapes for Software-related Quality Models. CoRR abs/1403.5432 (2014) - 2013
- [c11]Michael Kläs, Thomas Bauer, Ubaldo Tiberi:
Beyond Herding Cats: Aligning Quantitative Technology Evaluation in Large-Scale Research Projects. PROFES 2013: 80-92 - [i1]Michael Kläs, Constanza Lampasona, Jürgen Münch:
Adapting Software Quality Models: Practical Challenges, Approach, and First Empirical Results. CoRR abs/1312.1042 (2013) - 2012
- [c10]Stefan Wagner, Klaus Lochmann, Lars Heinemann, Michael Kläs, Adam Trendowicz, Reinhold Plösch, Andreas Seidl, Andreas Goeb, Jonathan Streit:
The Quamoco product quality modelling and assessment approach. ICSE 2012: 1133-1142 - [c9]Alois Mayr, Reinhold Plösch, Michael Kläs, Constanza Lampasona, Matthias Saft:
A Comprehensive Code-Based Quality Model for Embedded Systems: Systematic Development and Validation by Industrial Projects. ISSRE 2012: 281-290 - 2011
- [c8]Michael Kläs, Adam Trendowicz, Yasushi Ishigai, Haruka Nakao:
Handling Estimation Uncertainty with Bootstrapping: Empirical Evaluation in the Context of Hybrid Prediction Methods. ESEM 2011: 245-254 - [c7]Michael Kläs, Constanza Lampasona, Jürgen Münch:
Adapting Software Quality Models: Practical Challenges, Approach, and First Empirical Results. EUROMICRO-SEAA 2011: 341-348 - 2010
- [j3]Michael Kläs, Haruka Nakao, Frank Elberzhager, Jürgen Münch:
Support planning and controlling of early quality assurance by combining expert judgment and defect data - a case study. Empir. Softw. Eng. 15(4): 423-454 (2010) - [j2]Stefan Wagner, Manfred Broy, Florian Deißenböck, Michael Kläs, Peter Liggesmeyer, Jürgen Münch, Jonathan Streit:
Softwarequalitätsmodelle - Praxisempfehlungen und Forschungsagenda. Inform. Spektrum 33(1): 37-44 (2010) - [c6]Michael Kläs, Frank Elberzhager, Jürgen Münch, Klaus Hartjes, Olaf von Graevemeyer:
Transparent combination of expert and measurement data for defect prediction: an industrial case study. ICSE (2) 2010: 119-128
2000 – 2009
- 2009
- [c5]Stefan Wagner, Klaus Lochmann, Sebastian Winter, Andreas Goeb, Michael Klaes:
Quality models in practice: A preliminary analysis. ESEM 2009: 464-467 - [c4]Michael Kläs, Jens Heidrich, Jürgen Münch, Adam Trendowicz:
CQML Scheme: A Classification Scheme for Comprehensive Quality Model Landscapes. EUROMICRO-SEAA 2009: 243-250 - [c3]Michael Kläs, Frank Elberzhager, Rolf Hendrik van Lengen, Torsten Schulz, Jürgen Goebbels:
A Framework for the Balanced Optimization of Quality Assurance Strategies Focusing on Small and Medium Sized Enterprises. EUROMICRO-SEAA 2009: 335-342 - 2008
- [j1]Michael Kläs, Adam Trendowicz, Axel Wickenkamp, Jürgen Münch, Nahomi Kikuchi, Yasushi Ishigai:
The Use of Simulation Techniques for Hybrid Software Cost Estimation and Risk Analysis. Adv. Comput. 74: 115-174 (2008) - [c2]Michael Kläs, Frank Elberzhager, Haruka Nakao:
Managing software quality through a hybrid defect content and effectiveness model. ESEM 2008: 321-323 - [c1]Michael Kläs, Haruka Nakao, Frank Elberzhager, Jürgen Münch:
Predicting Defect Content and Quality Assurance Effectiveness by Combining Expert Judgment and Defect Data - A Case Study. ISSRE 2008: 17-26
Coauthor Index
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