  Dynamic Probabilistic Risk Assessment (DPRA) is a powerful concept that is
used to evaluate design and safety of complex industrial systems. A DPRA model
uses a conceptual system representation as a formal basis for simulation and
analysis. In this paper we consider an adaptive maintenance of DPRA models that
consist in modifying and extending a simplified model to a real-size DPRA
model. We propose an approach for quantitative maintainability assessment of
DPRA models created with an industrial modeling tool called PyCATSHOO. We
review and adopt some metrics from conceptual modeling, software engineering
and OO design for assessing maintainability of PyCATSHOO models. On the example
of well-known "Heated Room" test case, we illustrate how the selected metrics
can serve as early indicators of model modifiability and complexity. These
indicators would allow experts to make better decisions early in the DPRA model
development life cycle.
