Dynamic fault detection in context-aware adaptation

Abstract

Internetware applications are context-aware and adaptive to their environmental changes. Faulty adaptation may arise when these applications face unexpected situations. Such adaptation faults can be difficult to detect at design time. The recent Adaptation Finite-State Machine (A-FSM) approach proposes to statically analyze model-based context-aware applications for adaptation faults. However, this approach may suffer expressiveness and precision problems. To address these limitations, we propose an Adaptation Model (AM) approach. As compared with A-FSM, AM offers increased expressive power to model complex rules, and guarantees soundness in fault detection. Besides, AM deploys an efficient rule evaluation technique to cater for context-aware applications that are subject to continual environmental changes. We evaluated our AM approach using both simulated and real-world experiments with two applications. The experimental results confirmed that AM can detect real faults missed by A-FSM, and avoid false positives that were misreported otherwise.