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.

In a cloud environment, a cloud broker plays a vital role as an intermediary between cloud customers and providers, resolving issues and facilitating negotiations to balance customer preferences and provider profits. Over the past few years, numerous research articles have either directly or indirectly examined this area. Conducting a Systematic Mapping Study (SMS) on cloud brokerage is highly motivating as it offers a high-level overview of the research landscape, identifying trends, topics, gaps, and patterns within this dynamic and crucial field. This article presents an SMS conducted to categorize existing research, highlight underexplored areas, and map out the evolution and current state of cloud brokerage, providing valuable insights for researchers and practitioners. The SMS identified 91 relevant and reputable search spaces (journals and conferences) and 634 high-quality articles published from 2009 to 2022. Furthermore, we formulated and addressed eight significant research questions to clarify various aspects of the cloud broker field. The extracted information from the selected articles is included in a supplementary file, available online, offering valuable insights for research teams and developers interested in this domain.