The component is then deployed in a dedicated container, a MOCAS component too, in order to become adaptive. This state machine is later executed by the component in order to realize its behavior. The behavior of the component is specified with a UML state machine. The structure of a MOCAS component is specified with UML native elements. MOCAS is based on model-driven engineering techniques and only relies on the Unified Modeling Language (UML) to endow each software component with self-adaptive capabilities. We defined the MOCAS component model (Model Of Components for Adaptive Systems) to allow the construction of autonomic systems by using self-adaptive software components. In our approach, we focus on behavioral adaptation. Hitherto, current approaches focus on adaptations related to the structure of component-based systems by altering links between components. These capabilities are strongly tied to the self-adaptive one, which enables a system to modify its structure and its behavior while it is running. For administrators to be freed from tedious tasks and for systems to be more reactives, these systems, a.k.a autonomic systems, tend to be endowed with self-management capabilities such as self-configuration and self-healing. Software administrators, developers and designers need original means to deal with the growing complexity of IT systems. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. We provide a classification of different input types for such systems and analyse the limitations of each input type. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments.
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