With ubiquitous computing, autonomous cars, and cyber-physical systems (CPS), adaptive software becomes more and more important as computing is increasingly context-dependent. Role-based programming has been proposed to enable adaptive software design without the problem of scattering the context-dependent code. Adaptation is achieved by having objects play roles during runtime. With every role, the object's behavior is modified to adapt to the given context. In recent years, many role-based programming languages have been developed. While they greatly differ in the set of supported features, they all incur in large runtime overheads, resulting in inferior performance. The increased variability and expressiveness of the programming languages have a direct impact on the run-Time and memory consumption. In this paper we provide a detailed analysis of state-of-The-Art role-based programming languages, with emphasis on performance bottlenecks. We also provide insight on how to overcome these problems.