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Background: Self-awareness has been recently receiving a ention in computing systems for enriching autonomous so ware systems operating in dynamic environments. Objective: We aim to investigate the adoption of computational self-awareness concepts in autonomic so ware systems, and motivate future research directions on self-awareness and related problems. Method: We conducted a systemic literature review to compile the studies related to the adoption of self-awareness in so ware engineering and explore how self-awareness is engineered and incorporated in so ware systems. From 865 studies, 74 studies have been selected as primary studies. We have analysed the studies from multiple perspectives, such as motivation, inspiration, and engineering approaches, among others. Results: Results have shown that self-awareness has been used to enable self-adaptation in systems that exhibit uncertain and dynamic behaviour. ough the recent a empts to de ne and engineer self-awareness in so ware engineering, there is no consensus on the de nition of self-awareness. Also, the distinction between self-aware and self-adaptive systems has not been systematically treated. Conclusions: Our survey reveals that self-awareness for so ware systems is still a formative eld and that there is growing a ention to incorporate self-awareness for be er reasoning about the adaptation decision in autonomic systems. Many pending issues and open problems, outlining possible research directions. CCS Concepts: •General and reference → Surveys and overviews; General literature; •Social and professional topics → So ware selection and adaptation; •So ware and its engineering → So ware con guration management and version control systems;

Architecture-based self-adaptation has been recognised as one of the prominent ways to design autonomic systems, where self-manageable architectures tend to achieve the required level of dynamicity and compliance with the continual changing in QoS requirements during run-time. Self-awareness and self-expression have recently emerged as promising architectural concepts in the field of self-adaptive software. Self-aware architecture patterns are envisioned as enabler for self-adaptation, but they tend to provide limited support for the QoS run-time requirements. While the research community has developed in architecture quality management, patterns and tactics, addressing quality attributes in self-aware architectures has not been tackled yet. In this paper, we aim to provide quality-driven architectural patterns for emerging class of architecture enabled by the principles of self-awareness. We report on the feasibility of correlating QoS tactics with self-aware capabilities to better respond to QoS run-time requirements and trade-offs. We describe novel extensions which make the correlation between QoS tactics and self-awareness explicit. We quantitatively evaluate the feasibility, generality and fitness of the proposed approach, as well as its potential applicability to self-aware architectures. Though the proposed extensions can potentially benefit architectures which leverage on self-awareness, we use the case of cloud auto-scaling architecture.

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