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The article is a foreword for the SEAMS 2022 Special Issue in the ACM Transactions on Autonomous & Adaptive Systems. The Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) focuses on methods, techniques, processes, and tools to support the construction of self-adaptive and autonomous systems. The SEAMS 2022 symposium was held remotely due to the COVID-19 pandemic, with a smaller group meeting in person to discuss transferring research into practice. The special issue includes six papers that advance the research of self-adaptation into new domains and the software engineering of self-adaptive systems. The papers cover topics such as software-defined networks, human-machine teaming, self-adaptive testing, handling concept drift, predicting nonfunctional requirement violations, and managing serverless functions at the edge. The authors express gratitude to the reviewers and committees involved in the symposium and special issue. [Extracted from the article]

Trends such as the Internet of Things lead to a growing number of networked devices and to a variety of communication systems. Adding self-adaptive capabilities to these communication systems is one approach to reducing administrative effort and coping with changing execution contexts. Existing frameworks can help reducing development effort but are neither tailored toward the use in communication systems nor easily usable without knowledge in self-adaptive systems development. Accordingly, in previous work, we proposed REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. REACT addresses heterogeneity and distribution aspects of such systems and reduces development effort. In this article, we propose REACT-ION—an extension of REACT for situation awareness. REACT-ION offers a context management module that is able to acquire, store, disseminate, and reason on context data. The context management module is the basis for (i) proactive adaptation with REACT-ION and (ii) self-improvement of the underlying feedback loop. REACT-ION can be used to optimize adaptation decisions at runtime based on the current situation. Therefore, it can cope with uncertainty and situations that were not foreseeable at design time. We show and evaluate in two case studies how REACT-ION’s situation awareness enables proactive adaptation and self-improvement. [ABSTRACT FROM AUTHOR]

Modern cyber-physical systems (e.g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time. Assumptions about parts of the system made at design time may not hold at run time, especially when a system is deployed for long periods (e.g., over decades). Self-adaptation is designed to find reconfigurations of systems to handle such run-time inconsistencies. Planners can be used to find and enact optimal reconfigurations in such an evolving context. However, for systems that are highly configurable, such planning becomes intractable due to the size of the adaptation space. To overcome this challenge, in this paper we explore an approach that (a) uses machine learning to find Pareto-optimal configurations without needing to explore every configuration and (b) restricts the search space to such configurations to make planning tractable. We explore this in the context of robot missions that need to consider task timeliness and energy consumption. An independent evaluation shows that our approach results in high-quality adaptation plans in uncertain and adversarial environments. [ABSTRACT FROM AUTHOR]

Proactive latency-aware adaptation is an approach for self-adaptive systems that considers both the current and anticipated adaptation needs when making adaptation decisions, taking into account the latency of the available adaptation tactics. Since this is a problem of selecting adaptation actions in the context of the probabilistic behavior of the environment, Markov decision processes (MDPs) are a suitable approach. However, given all the possible interactions between the different and possibly concurrent adaptation tactics, the system, and the environment, constructing the MDP is a complex task. Probabilistic model checking has been used to deal with this problem, but it requires constructing the MDP every time an adaptation decision is made to incorporate the latest predictions of the environment behavior. In this article, we describe PLA-SDP, an approach that eliminates that runtime overhead by constructing most of the MDP offline. At runtime, the adaptation decision is made by solving the MDP through stochastic dynamic programming, weaving in the environment model as the solution is computed. We also present extensions that support different notions of utility, such as maximizing reward gain subject to the satisfaction of a probabilistic constraint, making PLA-SDP applicable to systems with different kinds of adaptation goals. [ABSTRACT FROM AUTHOR]