Many-objective Self-adaptation under Model Uncertainty

The field of uncertainty quantification and mitigation in software-intensive and self-adaptive systems is garnering increased interest, especially with the rise of statistical inference methodologies like Bayesian reasoning. These methods typically address uncertain quality attributes embedded within system models by adjusting model parameters. However, the uncertainty related to selecting a specific system model over plausible alternatives has received limited attention. Our work focuses on self-adaptation, exploring methods to tackle uncertainty in model selection. This includes scenarios where one model is chosen over competing alternatives to encapsulate the system’s understanding and anticipate future observations. Our proposed solution augments the conventional feedback loop of self-adaptive systems by combining Bayesian model averaging to mitigate uncertainty and many-objective optimization to take into account multiple, possibly many, dependability requirements at the same time. We carry out an empirical evaluation to study the effectiveness, cost, and scalability of the proposed approach using two case studies with increasing structural complexity and number of dependability requirements. Results show that our approach based on model averaging is significantly better than model selection in terms of satisfied requirements after adaptation (adaptation success frequency). We also show that our approach can deal with large model spaces ( \(\sim 10^{19}\) ) using efficient sampling methods rather than exhaustive model space exploration.