This paper explores and describes the state of the art for what concerns the model-driven approaches proposed in the literature to support reverse engineering. We conducted a systematic literature review on this topic with the aim to answer three research questions. We focus on various solutions developed for model-driven reverse engineering, outlining in particular the models they use and the transformations applied to the models. We also consider the tools used for model definition, extraction, and transformation and the level of automation reached by the available tools. The model-driven reverse engineering approaches are also analyzed based on various features such as genericity, extensibility, automation of the reverse engineering process, and coverage of the full or partial source artifacts. We describe in detail and compare fifteen approaches applying model-driven reverse engineering. Based on this analysis, we identify and indicate some hints on choosing a model-driven reverse engineering approach from the available ones, and we outline open issues concerning the model-driven reverse engineering approaches.

International audienceIn the last years, the growing complexity of the current applications has led to the design of self-adapting systems presenting self-* properties. These systems are composed of several autonomous interactive entities. They behave autonomously and present enhanced characteristics allowing them to handle dynamics coming from exogenous and endogenous changes. In this paper, we propose a set of criteria for the description and evaluation of the adaptive properties of such systems. They aim to provide a concrete mechanism to analyze the quality of the design of adaptive systems, to evaluate the effect of self-* properties on the performances and to compare the adaptive features of different systems. The criteria are grouped into different categories: methodological, architectural, intrinsic, and runtime evaluation. They have been identified and specified by analyzing several case studies, which address self-adaptivity issues through different approaches with different objectives in various application contexts

The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study.

Dynamic adaptivity is one of the most challenging non-functional issues to address during the engineering of information systems. Furthermore, it is even harder to evaluate. The definition of mechanisms through which adaptivity may be evaluated would be very useful both for the maintainability, reusability, and evolution of adaptive systems, and for the relations which may be established among adaptive systems (e.g., interaction, integration). In this paper we propose examples of metrics for dynamic adaptivity which may be considered a starting point for future research on (1) the feasibility related to the definition of measurable evaluation mechanisms for adaptive systems, and (2) the usability of these metrics as design hints in the development process of new adaptive systems and as formal approaches for the evaluation of existing adaptive systems.