Dear Colleagues,

The demands placed on software and system engineers have dramatically increased in recent years as the systems they develop have increased in complexity, size, and criticality as well. Novel system types, such as autonomous systems or cyber physical systems, and new technologies, such as Artificial Intelligence, are changing the technology landscape. Thus, supporting and mastering the development and evolution of such complex, software-intensive systems by new approaches is an important and growing research field in software and systems engineering.

A well-known approach to support the development of complex systems is model-based software and systems engineering. It represents the results of different development activities in one model and thus shifts from heterogeneous, document-based product models to consistent and interlinked product models. In practice, acceptance is often limited since experts have to transfer their established syntax, elements, and tools for system modeling to the more generic languages such as SysML. Although the objectives and benefits of model-based engineering are mentioned in countless research works, industry companies across branches are facing challenges when introducing and adapting to these techniques. Modeling is an essential aspect not only for designing the system but also for the long-term evolution of any system. We are interested in methods to overcome the numerous obstacles when applying model-based engineering across disciplines and introduction into practice.

Moreover, we welcome research works discussing current approaches and directions such as learning- and knowledge-based methods. For the development of AI-based complex systems, the main challenge is not to develop the best models/algorithms, but to provide support for the entire lifecycle—from a business idea, through the collection and management of data, software development managing both data and code, product deployment and operation, and to its evolution. There is a clear need for specific support of the software architecture for AI. On the other hand, the software engineering community observed that several software engineering tasks can be formulated as data analysis (learning) tasks and, thus, can be supported, for example, by using ML algorithms. This is the motivation for the research field of AI for software engineering (AI4SE), which has developed driven by the rapid increase in size and complexity of software systems and, in consequence, of software engineering tasks.

Finally, we are interested in advanced approaches on model-based product line development and life-cycle management for families of complex software systems. A challenge is to enable the long-term evolution of product lines. How to adapt to emerging  product line requirements and how to manage variability and product derivation are challenging duties as well as to ensure traceability between product line architecture, feature model, requirements, and implementation artifacts.

This Special Issue welcomes contributions regarding academic research and industrial contributions. We welcome technical papers presenting research and practical results, position papers, survey papers addressing the key problems, and solutions on any of the mentioned topics.

Topics of interest include but are not limited to the following:

• New modeling concepts in software and systems engineering;
• Knowledge-based and data-driven methods;
• Learning- and feedback-based approaches;
• Model-based requirements engineering;
• Traceability of requirements (modeling and analysis);
• Domain-specific models and their integration into meta-models;
• Automated generation of different views in development of complex systems;
• Architecture evaluation and quality aspects of software and system architectures;
• Model-driven engineering;
• Component-based software engineering;
• Automatic extraction and generation of architecture descriptions;
• Refactoring and evolving architecture design decisions and solutions;
• Architecture conformance;
• Linking architecture to requirements and/or implementation;
• Architecture frameworks and architecture description languages;
• Reusable architectural solutions and software architecture knowledge management;
• DevOps;
• AI/ML techniques for architecture;
• Agile development approaches for complex systems;
• State of the art and state of practice in model-based engineering for complex systems;
• Model-based approaches for dealing with families of products and variability;
• Model-based approaches for specific types of systems, such as:
• AI / ML systems;
• Cyber‒physical systems;
• Self-adaptive systems;
• Autonomous systems.

Prof. Dr. Andreas Rausch
Dr. Christoph Knieke
Guest Editors

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• model-based engineering
• software engineering
• software architecture
• artificial intelligence (AI)
• systems engineering