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Machine learning (ML)-based solutions are rapidly changing the landscape of many fields, including structural engineering. Despite their promising performance, these approaches are usually only demonstrated as proof-of-concept in structural…
Various stakeholders with different backgrounds are involved in Smart City projects. These stakeholders define the project goals, e.g., based on participative approaches, market research or innovation management processes. To realize these…
Deployment of distributed systems sets high requirements for procedures and tools for the complex testing of these systems. This work introduces a formal four-layered model for test generation mission on the basis of the component-based…
Complex systems' modeling and simulation are powerful ways to investigate a multitude of natural phenomena providing extended knowledge on their structure and behavior. However, enhanced modeling and simulation require integration of…
Machine learning (ML) teams often work on a project just to realize the performance of the model is not good enough. Indeed, the success of ML-enabled systems involves aligning data with business problems, translating them into ML tasks,…
Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to deliver a comprehensive overview of the current status quo…
Ensuring the quality of automated driving systems is a major challenge the automotive industry is facing. In this context, quality defines the degree to which an object meets expectations and requirements. Especially, automated vehicles at…
In the era of Model-as-a-Service, organizations increasingly rely on third-party AI models for rapid deployment. However, the dynamic nature of emerging AI applications, the continual introduction of new datasets, and the growing number of…
Requirements engineering (RE) activities for machine learning (ML) are not well-established and researched in the literature. Many issues and challenges exist when specifying, designing, and developing ML-enabled systems. Adding more focus…
Background: Contract-based Design (CbD) is a valuable methodology for software design that allows annotation of code and architectural components with contracts, thereby enhancing clarity and reliability in software development. It…
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…
Automated, data-driven quality management systems, which facilitate the transformation of data into useable information, are desired to enhance decision-making processes. Integration of accurate, reliable, and straightforward approaches…
In recent years, autonomous vehicles have attracted attention as one of the solutions to various social problems. However, autonomous driving software requires real-time performance as it considers a variety of functions and complex…
A Multi Robot System (MRS) is the infrastructure of an intelligent cyberphysical system, where the robots understand the need of the human, and hence cooperate together to fulfill this need. Modeling an MRS is a crucial aspect of designing…
The introduction of automated vehicles demands a way to prove their safe operation. However, validating the safety of automated vehicles is still an unsolved problem. While the scenario-based test approach seems to provide a possible…
Model-based safety assessment has been one of the leading research thrusts of the System Safety Engineering community for over two decades. However, there is still a lack of consensus on what MBSA is. The ambiguity in the identity of MBSA…
Context: Competitions for self-driving cars facilitated the development and research in the domain of autonomous vehicles towards potential solutions for the future mobility. Objective: Miniature vehicles can bridge the gap between…
Fault diagnosis is a crucial area of research in industry. Industrial processes exhibit diverse operating conditions, where data often have non-Gaussian, multi-mode, and center-drift characteristics. Data-driven approaches are currently the…
[Context] Model-based Systems Engineering (MBSE) comprises a set of models and techniques that is often suggested as solution to cope with the challenges of engineering complex systems. Although many practitioners agree with the arguments…
Despite the increasing testing operations of automated vehicles on public roads, media reports on incidents show that safety issues caused by automated driving systems persist to this day. Manufacturers face high development uncertainty…