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We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. We consider several problems arising in the design process, including training a system to be robust to rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs, then sampling these to generate specialized training and test data. More generally, such languages can be used to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems such as autonomous cars and robots, whose environment at any point in time is ascene, a configuration of physical objects and agents. We design a domain-specific language,Scenic, for describingscenariosthat are distributions over scenes and the behaviors of their agents over time.Sceniccombines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided byScenic’s domain-specific syntax. Finally, we applyScenicin multiple case studies for training, testing, and debugging neural networks for perception both as standalone components and within the context of a full cyber-physical system.