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Self-adaptive software systems are capable of adjusting their behavior at run-time to achieve certain objectives. Such systems typically employ analytical models specified at design-time to assess their characteristics at run-time and make the appropriate adaptation decisions. However, prior to system's deployment, engineers often cannot foresee the changes in the environment, requirements, and system's operational profile. Therefore, any analytical model used in this setting relies on underlying assumptions that if not held at run-time make the analysis and hence the adaptation decisions inaccurate. We present and evaluate FeatUre-oriented Self-adaptatION (FUSION) framework, which aims to solve this problem by learning the impact of adaptation decisions on the system's goals. The framework (1) allows for automatic online fine-tuning of the adaptation logic to unanticipated conditions, (2) reduces the upfront effort required for building such systems, and (3) makes the run-time analysis of such systems very efficient.
Elkhodary et al. (Sun,) studied this question.