# A Pragmatic Approach for Measuring Maintainability of DPRA Models

## TL;DR

This paper introduces a quantitative approach for assessing the maintainability of DPRA models created with PyCATSHOO, using metrics from software engineering to improve early decision-making in model development.

## Contribution

It proposes a novel set of metrics for evaluating DPRA model maintainability and demonstrates their effectiveness with a practical case study.

## Key findings

- Metrics can serve as early indicators of model modifiability.
- Selected metrics help assess complexity and maintainability.
- Approach supports better decision-making during model development.

## Abstract

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.