
\section{Conclusion}  We presented \NAME{} -- the first system for
  automatically evaluating the robustness of probabilistic programs against
  various noise patterns. Our study on \totalprogs benchmarks is the first
  systematic study of robustness of a diverse set of probabilistic programs. We
  highlight the benefits of robustness transformations when applied on different
  models. We show the tradeoffs between the level of robustness and the time
  overhead for different transformations, which can allow users deploy those
  robust model versions \mbox{that best fit their needs.}


\section*{Acknowledgements}


This research was supported in part by NSF Grants No.
CCF-1846354, CCF-1956374, CCF-2008883, a research award from C3.ai Digital Transformation Institute, and Facebook PhD Fellowship.

