Component-based software architecture enables software architects to design complex systems by composing components that interact through well-defined, syntactically specified interfaces. A special kind of component we investigated in our previous work is the analysis components. Analysis components support the evaluation and prediction of system’s functional and non-functional properties. Evaluating these properties early in the development process helps optimize system performance and ensure compliance with requirements. While approaches for modeling and analyzing such systems, such as the Palladio approach, support syntactic validation of the composition, they often lack mechanisms to ensure the semantic soundness of compositions. In this paper, we present a model transformation approach to help architects ensure that system models are semantically sound and behave as expected. This approach enables the transformation of Palladio models into MontiArc models, allowing architects to enrich their system representations with semantic constraints and validate these constraints with the MontiArc workbench. This ensures that component interactions are consistent with both structural composition and intended semantics. We evaluate our approach through two different case studies. From these case studies, we derived several scenarios with varying constraints and states to assess the accuracy and performance of our approach. To evaluate accuracy, we examined our approach’s ability to check semantic constraints and detect violations. We observed high accuracy across the case studies. For performance, we analyze time complexity in different constraint types. The approach performed well when applied to arithmetic constraints, with its effectiveness decreasing when applied to more complex string-centered constraints.

Many organizations are involved in the development of complex systems, e.g., cyber-physical systems. Organizations work collaboratively to describe these systems, using models, which are developed using multiple languages and tools. The models may contain intellectual property that must be protected from other parties, including other contributors. To enable the ongoing exchange of models and to ensure intellectual property protection, our new idea is to use encrypted deltas, i.e., arbitrary changes made to a model. These encrypted deltas are stored on a chain, which we call Deltachain. Encryption enables free exchange of the Deltachain, e.g., on third-party commercial file storage servers. Collaborators involved in the development of the model can access the encrypted Deltachain, decrypt the parts to which they have access, and then work with those decrypted parts which are created by applying the deltas. Subsequently, the collaborators can encrypt their deltas to the model parts and append the encrypted deltas to the Deltachain. Our vision is the use of this Deltachain by collaborating organizations as a single source of truth.

Microservice-based systems are usually developed according to agile practices like DevOps, which enables rapid and frequent releases to promptly react and adapt to changes. Monitoring is a key enabler for these systems, as they allow to continuously get feedback from the field and support timely and tailored decisions for a quality-driven evolution. In the realm of monitoring tools available for microservices in the DevOps-driven development practice, each with different features, assumptions, and performance, selecting a suitable tool is an as much difficult as impactful task. This article presents the results of a systematic study of the grey literature we performed to identify, classify and analyze the available monitoring tools for DevOps and microservices. We selected and examined a list of 71 monitoring tools, drawing a map of their characteristics, limitations, assumptions, and open challenges, meant to be useful to both researchers and practitioners working in this area. Results are publicly available and replicable. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.