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More than a decade ago, the research topic models@run.time was coined. Since then, the research area has received increasing attention. Given the prolific results during these years, the current outcomes need to be sorted and classified. Furthermore, many gaps need to be categorized in order to further develop the research topic by experts of the research area but also newcomers. Accordingly, the paper discusses the principles and requirements of models@run.time and the state of the art of the research line. To make the discussion more concrete, a taxonomy is defined and used to compare the main approaches and research outcomes in the area during the last decade and including ancestor research initiatives. We identified and classified 275 papers on models@run.time, which allowed us to identify the underlying research gaps and to elaborate on the corresponding research challenges. Finally, we also facilitate sustainability of the survey over time by offering tool support to add, correct and visualize data.

Role-based modeling has been proposed almost 40 years ago as a means to model complex and dynamic domains, because roles are able to capture both context-dependent and collaborative behavior of objects. Unfortunately, while several researchers have introduced the notion of roles to modeling and programming languages, only few have captured both the relational and the context-dependent nature of roles. In this work, we classify various proposals since 2000 and show the discontinuity and fragmentation of the whole research field. To overcome discontinuity, we propose a family of metamodels for role-based modeling languages. Each family member corresponds to a design decision captured in a feature model. In this way, it becomes feasible to generate a metamodel for each role-based approach. This allows for the combination and improvement of the different role-based modeling and programming languages and paves the way to reconcile the research field.

This paper presents an approach to operate multi-objective self-optimizing software systems based on the models@run.time paradigm. In contrast to existing approaches, which are usually specific to a single or selected set of objectives (e.g., performance and/or reliability), the presented approach is generic in that it allows the software architect to model the relevant concerns of interest to self-optimization. At runtime, these models are interpreted and used to generate optimization problems. To evaluate the applicability of the approach, a scalability analysis is provided, showing the approach’s feasibility for at least two objectives.