The Generation and Evolution of Adaptation Rules in Requirements Driven Self-Adaptive Systems

One of the challenges in self-adaptive software systems is to make adaptation plans in response to possible changes. A good plan mechanism shall have the capability of: 1) selecting the most appropriate adaptation actions in response to changes both in the environment and requirements, 2) making adaptation decisions efficiently to react timely to arising situations at run-time. In existing approaches for plan process, rule-based adaptation provides an efficient offline planning method. However, it can react neither to changeable requirements nor to unexpected environment changes. On the contrary, goal-based and utility-based approaches provide online planning mechanisms, which can well handle a highly uncertain environment with dynamically changing requirements and environment. However, online adaptation decision making is often computationally expensive and may encounter less-efficiency problems. The aim of our research is to improve the planning processin requirements driven self-adaptive systems, i.e., enabling the self-adaptive system to efficiently make adaptation plans to cope with the dynamic environment and changeable requirements. To achieve such advantages, we propose a solution to enhance the traditional rule-based adaptation with a rule generation and a rule evolution process, so that the proposed approach can maintain the advantages of efficient planning process while being enhanced with the capability of dealing with runtime uncertainty.