  Simulation experiments are typically conducted repeatedly during the model
development process, for example, to re-validate if a behavioral property still
holds after several model changes. Approaches for automatically reusing and
generating simulation experiments can support modelers in conducting simulation
studies in a more systematic and effective manner. They rely on explicit
experiment specifications and, so far, on user interaction for initiating the
reuse. Thereby, they are constrained to support the reuse of simulation
experiments in a specific setting. Our approach now goes one step further by
automatically identifying and adapting the experiments to be reused for a
variety of scenarios. To achieve this, we exploit provenance graphs of
simulation studies, which provide valuable information about the previous
modeling and experimenting activities, and contain meta-information about the
different entities that were used or produced during the simulation study. We
define provenance patterns and associate them with a semantics, which allows us
to interpret the different activities, and construct transformation rules for
provenance graphs. Our approach is implemented in a Reuse and Adapt framework
for Simulation Experiments (RASE) which can interface with various modeling and
simulation tools. In the case studies, we demonstrate the utility of our
framework for a) the repeated sensitivity analysis of an agent-based model of
migration routes, and b) the cross-validation of two models of a cell signaling
pathway.
