Requirements-Driven Dynamic Adaptation to Mitigate Runtime Uncertainties for Self-Adaptive Systems
This addresses the challenge of mitigating runtime uncertainties for self-adaptive systems, which is incremental as it builds on existing adaptation methods by focusing on the requirements phase.
The paper tackles runtime uncertainty in self-adaptive systems by proposing REDAPT, a requirements-driven adaptation approach using adaptive goal models and logic specifications, demonstrated in an Intelligent Transportation System example with simulation evaluation.
Self-adaptive systems are capable of adjusting their behavior to cope with the changes in environment and itself. These changes may cause runtime uncertainty, which refers to the system state of failing to achieve appropriate reconfigurations. However, it is often infeasible to exhaustively anticipate all the changes. Thus, providing dynamic adaptation mechanisms for mitigating runtime uncertainty becomes a big challenge. This paper suggests solving this challenge at requirements phase by presenting REDAPT, short for REquirement-Driven adAPTation. We propose an adaptive goal model (AGM) by introducing adaptive elements, specify dynamic properties of AGM by providing logic based grammar, derive adaptation mechanisms with AGM specifications and achieve adaptation by monitoring variables, diagnosing requirements violations, determining reconfigurations and execution. Our approach is demonstrated with an example from the Intelligent Transportation System domain and evaluated through a series of simulation experiments.