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Abstract. Model transformation techniques typically operate under the
assumption that models do not contain uncertainty. In the presence of
uncertainty, this forces modelers to either postpone working or to arti-
ficially remove it, with negative impacts on software cost and quality.
Instead, we propose a technique to adapt existing model transforma-
tions so that they can be applied to models even if they contain un-
certainty, thus enabling the use of transformations earlier. Building on
earlier work, we show how to adapt graph rewrite-based model transfor-
mations to correctly operate on May uncertainty, a technique that allows
explicit uncertainty to be expressed in any modeling language. We eval-
uate our approach on the classic Object-Relational Mapping use case,
experimenting with models of varying levels of uncertainty.