A Pragmatic Approach for Measuring Maintainability of DPRA Models
This work addresses the challenge of maintaining DPRA models for experts in industrial safety and risk assessment, but it is incremental as it applies existing metrics to a specific domain.
The paper tackled the problem of quantitatively assessing the maintainability of Dynamic Probabilistic Risk Assessment (DPRA) models, proposing an approach that adapts metrics from conceptual modeling and software engineering to evaluate model modifiability and complexity using the 'Heated Room' test case.
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.