The ever-growing complexity of software systems coupled with their stringent availability requirements are challenging the manual management of software after its deployment. This has motivated the development of self-adaptive software systems. Self-adaptation endows a software system with the ability to satisfy certain objectives by automatically modifying its behavior at runtime. While many promising approaches for the construction of self-adaptive software systems have been developed, the majority of them ignore the uncertainty underlying the adaptation. This has been one of the key inhibitors to widespread adoption of self-adaption techniques in risk-averse real-world applications. Uncertainty in this setting is a vaguely understood term. In this paper, we characterize the sources of uncertainty in self-adaptive software system, and demonstrate its impact on the system's ability to satisfy its objectives. We then provide an alternative notion of optimality that explicitly incorporates the uncertainty underlying the knowledge (models) used for decision making. We discuss the state-of-the-art for dealing with uncertainty in this setting, and conclude with a set of challenges, which provide a road map for future research. © 2013 Springer-Verlag.
Esfahani, N., & Malek, S. (2013). Uncertainty in self-adaptive software systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7475 LNCS, pp. 214–238). https://doi.org/10.1007/978-3-642-35813-5_9
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