A model-based end-to-end toolchain for the probabilistic analysis of complex systems


We present a model-based environment for the probabilistic analysis of systems operating under uncertain conditions. This uncertainty may result from either the environments in which they operate or the platforms on which they execute. Available probabilistic analysis methods require to capture the system specification using languages that are semantically very close to Markov Chains. However, designers use model-based environments working at much higher abstraction levels. We present an integrated tool, called StoNES (Stochastic analysis of Networked Embedded Systems), that automates the model transformation and probabilistic analysis of systems. We apply our translation and analysis methodology to explore the trade-off between sensor accuracy and computational speed for the vision algorithm of an autonomous helicopter system.

2010 IEEE International Conference on Automation Science and Engineering