Explicit Modelling and Treatment of Repair in Prediction of Dependability

In engineering practice, multiple repair actions are considered carefully by designers, and their success or failure defines further control actions and the evolution of the system state. Such treatment is not fully supported by the current state-of-the-art in dependability analysis. We propose a novel approach for explicit modelling and analysis of repairable systems, and describe an implementation, which builds on HiP-HOPS, a method and tool for model-based synthesis of dependability evaluation models. HiP-HOPS is augmented with Pandora, a temporal logic for the qualitative analysis of Temporal Fault Trees (TFTs), and capabilities for quantitative dependability analysis via Stochastic Activity Networks (SAN). Dependability prediction is achieved via explicit modelling of local failure and repair events in a system model and then by: (i) propagation of local effects through the model and synthesis of repair-aware TFTs for the system, (ii) qualitative analysis of TFTs that respects both failure and repair logic and (iii) quantification of dependability via translation of repair-aware TFTs into SAN. The approach provides insight into the effects of multiple and alternative failure and repair scenarios, and can thus be useful in reconfigurable systems that typically employ software to utilise functional redundancies in a variety of ways.