Abstract Ensembles and swarms occur everywhere in nature, in social and technical contexts. Their modeling and programming form a significant challenge in modern software systems engineering. While it is well-known how self-adaptive or autonomic systems can be designed advantageously, the effective modeling and programming of ensembles often require the engineering of new software languages, which necessitates a tremendous development effort. This paper presents a context-role-oriented approach for modeling the structure and behavior of ensembles. We contribute three design patterns for frequently occurring ensemble types, developed via context-role-oriented modeling. Additionally, we show the feasibility of nesting design patterns to describe the behavior of nested swarms.

In the future, technical systems, e.g., cyber-physical systems will increasingly become adaptive to changing contexts. However, programming context-adaptive systems is challenging. The context-specific behavior as well as when contexts change their activeness must be specified. Common general-purpose object-oriented languages require encoding the context-specific behavior in if-cascades. Standard polymorphism is insufficient to express the changing behavior of objects in different contexts because the complexity rises with the combinatorial explosion of a large, potentially multi-dimensional context space. Moreover, specifying when contexts are active has to be implemented as part of the application logic. In conclusion, there are two problems: the definition of context-specific behavior and the management of context changes. In this paper, we present a framework for the Julia programming language to develop context-adaptive systems. The framework also enables context-adaptive equation-based modeling. For context management, Petri nets are utilized. Julia was chosen for the implementation due to its simulation ecosystem, rich metaprogramming, and multiple dispatch, which enables precise specification of behavioral variants. We evaluate our approach by using two examples. The first scenario is a smart home control application. The second example shows how our framework can be used together with equation-based modeling for simulation.