Adaptive Requirement Engineering through Digital Twin Feedback Loops

Traditional Requirement Engineering (RE) relies on static specifications that frequently fail to accommodate evolving stakeholder needs, environmental changes, and runtime system feedback. This paper proposes a novel Adaptive Requirement Engineering framework driven by Digital Twin Feedback Loops (ADTF-RE) to enable dynamic adaptation of requirements throughout the system lifecycle. The methodology integrates Digital Twin (DT) technology with continuous feedback mechanisms to sense requirement deviations, analyze context changes, and update requirement models in near real time. A formal mathematical model, experimental simulations, five analytical figures, and comparative results are provided to validate the proposed approach. Evaluations conducted on a smart healthcare monitoring scenario demonstrate that ADTF-RE reduces adaptation latency by approximately 64% relative to traditional RE and 55% relative to static Digital Twin baselines, while achieving a stakeholder satisfaction score of 4.6/5.0. The results confirm that continuous feedback loops substantially improve requirement adaptability, responsiveness, and overall system alignment in dynamic cyber-physical environments