Related papers: Engineering Decisions in MBSE: Insights for a Deci…
[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…
Designing, assuring and releasing safe automated vehicles is a highly interdisciplinary process. As complex systems, automated driving systems will inevitably be subject to emergent properties, i. e., the properties of the overall system…
Complex systems are typically designed collaboratively by stakeholders from different domains. This multi viewpoints paradigm promotes the separation of concerns since separate teams, from different business viewpoints, build partial models…
Model-Based Systems Engineering (MBSE) is a development approach aiming to build correct-by-construction systems, provided the use of clear, unambiguous and complete models to describe them along the design process. The approach is…
Engineering models created in Model-Based Systems Engineering (MBSE) environments contain detailed information about system structure and behavior. However, they typically lack symbolic planning semantics such as preconditions, effects, and…
Software architecture decision-making is critical to the success of a software system as software architecture sets the structure of the system, determines its qualities, and has far-reaching consequences throughout the system life cycle.…
Decision-making is a key software engineering skill. Developers constantly make choices throughout the software development process, from requirements to implementation. While prior work has studied developer decision-making, the choices…
Nowadays, collaborative modeling performed by multiple stakeholders is gaining a growing interest in both academia and practice. However, it poses a set of research challenges, such as large and complex models management, support for…
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…
The specification of requirements and tests are crucial activities in automotive development projects. However, due to the increasing complexity of automotive systems, practitioners fail to specify requirements and tests for distributed and…
Multimodal summarization integrating information from diverse data modalities presents a promising solution to aid the understanding of information within various processes. However, the application and advantages of multimodal…
Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving vehicles. In order to use machine learning in a safety-critical…
Innovation and entrepreneurship have a very special role to play in creating sustainable development in the world. Engineering design plays a major role in innovation. These are not new facts. However this added to the fact that in current…
We introduce SysML-Sec, a SysML-based Model-Driven Engineering environment aimed at fostering the collaboration between system designers and security experts at all methodological stages of the development of an embedded system. A central…
Purpose: Continuous Software Engineering (CSE) promises improved efficiency, quality, and responsiveness in software-intensive organizations. However, fully adopting CSE is often constrained by complex products, legacy systems,…
Microservices are quite widely impacting on the software industry in recent years. Rapid evolution and continuous deployment represent specific benefits of microservice-based systems, but they may have a significant impact on non-functional…
Context: The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. However, the full potential of machine learning for…
Contemporary intelligent systems incorporate software components, including machine learning components. As they grow in complexity and data volume such machine learning systems face unique quality challenges like scalability and…
The purpose of this article is to describe an adaptive decision-making support model aimed at improving the efficiency of engineering infrastructure reconstruction program management in the context of developing the architecture and work…
Machine learning has been increasingly applied in climate modeling on system emulation acceleration, data-driven parameter inference, forecasting, and knowledge discovery, addressing challenges such as physical consistency, multi-scale…