Related papers: A Concept for a Qualifiable (Meta)-Modeling Framew…
The use of domain-specific modeling for development of complex (cyber-physical) systems is gaining increasing acceptance in the industrial environment. Domain-specific modeling allows complex systems and data to be abstracted for a more…
Most recent software related accidents have been system accidents. To validate the absence of system hazards concerning dysfunctional interactions, industrials call for approaches of modeling system safety requirements and interaction…
As Large Language Models (LLMs) advance, their potential for widespread societal impact grows simultaneously. Hence, rigorous LLM evaluations are both a technical necessity and social imperative. While numerous evaluation benchmarks have…
Meta-learning, also known as ``learning to learn'', enables models to acquire great generalization abilities by learning from various tasks. Recent advancements have made these models applicable across various fields without data…
Long-term autonomy of robotic systems implicitly requires dependable platforms that are able to naturally handle hardware and software faults, problems in behaviors, or lack of knowledge. Model-based dependable platforms additionally…
Nowadays, companies are highly exposed to cyber security threats. In many industrial domains, protective measures are being deployed and actively supported by standards. However the global process remains largely dependent on document…
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 - though not exclusive - prerequisites for this are the…
A software architect uses quality requirements to design the architecture of a system. However, it is essential to ensure that the system's final architectural design achieves the standard quality requirements. The existing architectural…
Flat optics is now judged by more than a strong simulation or a single laboratory demonstration. To reach release, a device must survive a chain of handoffs: requirements, model selection, verification, layout release, fabrication,…
Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models.…
Todays industrial control systems consist of tightly coupled components allowing adversaries to exploit security attack surfaces from the information technology side, and, thus, also get access to automation devices residing at the…
In model-driven engineering, models abstract the relevant features of software artefacts and model transformations act on them automating complex tasks of the development process. It is, thus, crucially important to provide pragmatic,…
Model-based engineering promises to boost productivity and quality of complex systems development. In the context of safety-critical systems, a traditionally highly regulated and conservative domain, the use of models gained importance in…
System complexity has become ubiquitous in the design, assessment, and implementation of practical and useful cyber-physical systems. This increased complexity is impacting the management of models necessary for designing cyber-physical…
Systems modeling practice lacks security analysis tools that can interface with modeling languages to facilitate security by design. Security by design is a necessity in the age of safety critical cyber-physical systems, where security…
Cyber-physical systems, such as learning robots and other autonomous systems, employ high-integrity software in their safety-critical control. This software is developed using a range of tools some of which need to be qualified for this…
The quality and correct functioning of software components embedded in electronic systems are of utmost concern especially for safety and mission-critical systems. Model-based testing and formal verification techniques can be employed to…
Software quality models provide either abstract quality characteristics or concrete quality measurements; there is no seamless integration of these two aspects. Reasons for this include the complexity of quality and the various quality…
Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making…
Assessing and improving the quality of data are fundamental challenges for data-intensive systems that have given rise to applications targeting transformation and cleaning of data. However, while schema design, data cleaning, and data…