Abstract
Self-adaptive systems research is expanding as systems professionals recognize the importance of automation for managing the growing complexity, scale, and scope of software systems. The current approach to designing such systems is ad hoc, varied, and fractured, often resulting in systems with parts of multiple, sometimes poorly compatible designs. In addition to the challenges inherent to all software, this makes evaluating, understanding, comparing, maintaining, and even using such systems more difficult. This paper discusses the importance of systematic design and identifies the dimensions of the self-adaptive system design space. It identifies key design decisions, questions, and possible answers relevant to the design space, and organizes these into five clusters: observation, representation, control, identification, and enacting adaptation. This characterization can serve as a standard lexicon, that, in turn, can aid in describing and evaluating the behavior of existing and new self-adaptive systems. The paper also outlines the future challenges for improving the design of self-adaptive systems.
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Brun, Y. et al. (2013). A Design Space for Self-Adaptive Systems. In: de Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds) Software Engineering for Self-Adaptive Systems II. Lecture Notes in Computer Science, vol 7475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35813-5_2
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DOI: https://doi.org/10.1007/978-3-642-35813-5_2
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