Automatic Adaptation Rule Optimization via Large Language Models

Rule-based adaptation is a foundational approach to self-adaptation, characterized by its human readability and rapid response. However, building high-performance and robust adaptation rules is often a challenge because it essentially involves searching for the optimal design in a complex (variable) space. In response, this paper attempts to employ large language models (LLMs) as an optimizer to construct and optimize adaptation rules, leveraging the common sense and reasoning capabilities inherent in LLMs. Preliminary experiments conducted in SWIM have validated the effectiveness and limitations of our method.