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Self-adaptation equips a computing system with a feedback loop that enablesit dealing with change caused by uncertainties during operation, such aschanging availability of resources and fluctuating workloads. To ensure thatthe system complies with the adaptation goals, recent research suggests the useof formal techniques at runtime. Yet, existing approaches have threelimitations that affect their practical applicability: (i) they ignorecorrectness of the behavior of the feedback loop, (ii) they rely on exhaustiveverification at runtime to select adaptation options to realize the adaptationgoals, which is time and resource demanding, and (iii) they provide limited orno support for changing adaptation goals at runtime. To tackle theseshortcomings, we present ActivFORMS (Active FORmal Models for Self-adaptation).ActivFORMS contributes an end-to-end approach for engineering self-adaptivesystems, spanning four main stages of the life cycle of a feedback loop:design, deployment, runtime adaptation, and evolution. We also presentActivFORMS-ta, a tool-supported instance of ActivFORMS that leverages timedautomata models and statistical model checking at runtime. We validate theresearch results using an IoT application for building security monitoring thatis deployed in Leuven. The experimental results demonstrate that ActivFORMSsupports correctness of the behavior of the feedback loop, achieves theadaptation goals in an efficient way, and supports changing adaptation goals atruntime.

ActivFORMS: A Formally-Founded Model-Based Approach to Engineer Self-Adaptive Systems