When a self-adaptive system detects that its adaptation goals may be compromised, it needs to determine how to adapt to ensure its goals. To that end, the system can analyze the possible options for adaptation, i.e., the adaptation space, and pick the best option that achieves the goals. Such analysis can be resource and time consuming, in particular when rigorous analysis methods are applied. Hence, exhaustively analyzing all options may be infeasible for systems with large adaptation spaces. This problem is further complicated as the adaptation options typically include uncertainty parameters that can only be resolved at runtime. In this paper, we present a machine learning approach to tackle this problem. This approach enhances the traditional MAPE-K feedback loop with a learning module that selects subsets of adaptation options from a large adaptation space to support the analyzer with performing efficient analysis. We instantiate the approach for two concrete learning techniques, classification and regression, and evaluate the approaches for two instances of an Internet of Things application for smart environment monitoring with different sizes of adaptation spaces. The evaluation shows that both learning approaches reduce the adaptation space significantly without noticeable effect on realizing the adaptation goals.

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Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

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Artifacts support evaluating new research results and help comparing them with the state of the art in a field of interest. Over the past years, several artifacts have been introduced to support research in the field of self-adaptive systems. While these artifacts have shown their value, it is not clear to what extent these artifacts support research on problems in self-adaptation that are relevant to industry. This paper provides a set of guidelines for artifacts that aim at supporting industry-relevant research on selfadaptation. The guidelines that are grounded on data obtained from a survey with practitioners were derived during working sessions at the 17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. Artifact providers can use the guidelines for aligning future artifacts with industry needs; they can also be used to evaluate the industrial relevance of existing artifacts. We also propose an artifact template.

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