Embedded systems are often assembled from black box components. System-level analyses, including verification and timing analysis, typically assume the system description, such as RTL or source code, as an input. There is therefore a need to automatically generate formal models of black box components to facilitate analysis.

We propose a new method to generate models of realtime embedded systems based on machine learning from execution traces, under a given hypothesis about the system's model of computation. Our technique is based on a novel formulation of the model generation problem as learning a dependency graph that indicates partial ordering between tasks. Tests based on an industry case study demonstrate that the learning algorithm can scale up and that the deduced system model accurately reflects dependencies between tasks in the original design. These dependencies help us formally prove properties of the system and also extract data dependencies that are not explicitly stated in the specifications of black box components.