Requirements-Driven Dynamic Adaptation to Mitigate Runtime Uncertainties for Self-Adaptive Systems

TL;DR

This paper introduces REDAPT, a requirements-driven approach for dynamic adaptation in self-adaptive systems to handle runtime uncertainties, using adaptive goal models and logic-based specifications, demonstrated in intelligent transportation systems.

Contribution

It presents a novel adaptive goal model and a requirements-driven framework for runtime adaptation, addressing the challenge of mitigating uncertainties without exhaustive anticipation.

Findings

Effective in reducing requirement violations in simulations

Demonstrated applicability in intelligent transportation systems

Provides a systematic approach for dynamic reconfiguration

Abstract

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.