Probabilistic programming languages offer an intuitive way to model uncertainty by representing complex probability models as simple probabilistic programs. Probabilistic programming systems (PP systems) hide the complexity of inference algorithms away from the program developer. Unfortunately, if a failure occurs during the run of a PP system, a developer typically has very little support in finding the part of the probabilistic program that causes the failure in the system. This paper presents Storm, a novel general framework for reducing probabilistic programs. Given a probabilistic program (with associated data and inference arguments) that causes a failure in a PP system, Storm finds a smaller version of the program, data, and arguments that cause the same failure. Storm leverages both generic code and data transformations from compiler testing and domain-specific, probabilistic transformations. The paper presents new transformations that reduce the complexity of statements and expressions, reduce data size, and simplify inference arguments (e.g., the number of iterations of the inference algorithm). We evaluated Storm on 47 programs that caused failures in two popular probabilistic programming systems, Stan and Pyro. Our experimental results show Storms effectiveness. For Stan, our minimized programs have 49% less code, 67% less data, and 96% fewer iterations. For Pyro, our minimized programs have 58% less code, 96% less data, and 99% fewer iterations. We also show the benefits of Storm when debugging probabilistic programs.