Rationalism with a dose of empiricism: Case-based reasoning for requirements-driven self-adaptation

Requirements-driven approaches provide an effective mechanism for self-adaptive systems by reasoning over their runtime requirements models to make adaptation decisions. However, such approaches usually assume that the relations among alternative behaviours, environmental parameters and requirements are clearly understood, which is often simply not true. Moreover, they do not consider the influence of the current behaviour of an executing system on adaptation decisions. In this paper, we propose an improved requirements-driven self-adaptation approach that combines goal reasoning and case-based reasoning. In the approach, past experiences of successful adaptations are retained as adaptation cases, which are described by not only requirements violations and contexts, but also currently deployed behaviours. The approach does not depend on a set of original adaptation cases, but employs goal reasoning to provide adaptation solutions when no similar cases are available. And case-based reasoning is used to provide more precise adaptation decisions that better reflect the complex relations among requirements violations, contexts, and current behaviours by utilizing past experiences. Our experimental study with an online shopping benchmark shows that our approach outperforms both requirements-driven approach and case-based reasoning approach in terms of adaptation effectiveness and overall quality of the system.