113 citations · 227 across the 11 of their papers we have counts for
11 papers
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
The European Machinery Directive and related harmonized standards do consider that software is used to generate safety-relevant behavior of the machinery but do not consider all ki…
Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception
Janek Groß, Rasmus Adler, Michael Kläs +3
Data-driven models (DDM) based on machine learning and other AI techniques play an important role in the perception of increasingly autonomous systems. Due to the merely implicit d…
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 +1
In the future, AI will increasingly find its way into systems that can potentially cause physical harm to humans. For such safety-critical systems, it must be demonstrated that the…
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models
Pascal Gerber, Lisa Jöckel, Michael Kläs
Outcomes of data-driven AI models cannot be assumed to be always correct. To estimate the uncertainty in these outcomes, the uncertainty wrapper framework has been proposed, which…
From Complexity Measurement to Holistic Quality Evaluation for Automotive Software Development
Jens Heidrich, Michael Kläs, Andreas Morgenstern +4
In recent years, the role and the importance of software in the automotive domain have changed dramatically. Being able to systematically evaluate and manage software quality is be…
Hardening of Artificial Neural Networks for Use in Safety-Critical Applications -- A Mapping Study
Rasmus Adler, Mohammed Naveed Akram, Pascal Bauer +6
Context: Across different domains, Artificial Neural Networks (ANNs) are used more and more in safety-critical applications in which erroneous outputs of such ANN can have catastro…
Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview
Michael Kläs
Quantifying and managing uncertainties that occur when data-driven models such as those provided by AI and machine learning methods are applied is crucial. This whitepaper provides…
Operationalised product quality models and assessment: The Quamoco approach
Stefan Wagner, Andreas Goeb, Lars Heinemann +8
Software quality models provide either abstract quality characteristics or concrete quality measurements; there is no seamless integration of these two aspects. Reasons for this in…
The Quamoco Product Quality Modelling and Assessment Approach
Stefan Wagner, Klaus Lochmann, Lars Heinemann +6
Published software quality models either provide abstract quality attributes or concrete quality assessments. There are no models that seamlessly integrate both aspects. In the pro…
Comprehensive Landscapes for Software-related Quality Models
Michael Kläs, Jens Heidrich, Jürgen Münch +1
Managing quality (such as service availability or process adherence) during the development, operation, and maintenance of software(-intensive) systems and services is a challengin…
Model-based Product Quality Evaluation with Multi-Criteria Decision Analysis
Adam Trendowicz, Michael Kläs, Constanza Lampasona +3
The ability to develop or evolve software or software-based systems/services with defined and guaranteed quality in a predictable way is becoming increasingly important. Essential…