Compositional Analysis of Probabilistic Timed Graph Transformation Systems

Abstract

The analysis of behavioral models is of high importance for cyber-physical systems, as the systems often encompass complex behavior based on e.g. concurrent components with mutual exclusion or probabilistic failures on demand. The rule-based formalism of Probabilistic Timed Graph Transformation Systems (PTGTSs) is a suitable choice when the models representing states of the system can be understood as graphs and timed and probabilistic behavior is important. However, model checking PTGTSs is limited to systems with rather small state spaces. We present an approach for the analysis of large-scale systems modeled as PTGTSs by systematically decomposing their state spaces into manageable fragments. To obtain qualitative and quantitative analysis results for a large-scale system, we verify that results obtained for its fragments serve as overapproximations for the corresponding results of the large-scale system. Hence, our approach allows for the detection of violations of qualitative and quantitative safety properties for the large-scale system under analysis. We consider a running example in which shuttles drive on tracks of a large-scale topology and autonomously coordinate their local behavior with other shuttles nearby. For this running example, we verify that (a) shuttles can always make the expected forward progress using several properties, (b) shuttles never collide, and (c) shuttles are unlikely to execute emergency brakes in two scenarios. In our evaluation, we apply an implementation of our approach in the tool  AutoGraph  to our running example.