Semi-automatic Identification and Representation of Subsystem Variability in Simulink Models

Abstract

This paper presents a semi-automated framework for identifying and representing different kinds of variability in Simulink models. Based on the observed variants found in similar subsystem patterns inferred using Simone, a text-based model clone detection tool, we propose a set of variability operators for Simulink models. By applying these operators to six example systems, we are able to represent the variability in their similar subsystem patterns as a single subsystem template directly in the Simulink environment. The product of our framework is a single consolidated subsystem model capable of expressing the observed variability across all instances of each inferred pattern. The process of pattern inference and variability analysis is largely automated and can be easily applied to other collections of Simulink models. The framework is aimed at providing assistance to engineers to identify, understand, and visualize patterns of subsystems in a large model set. This understanding may help in reducing maintenance effort and bug identification at an early stage of the software development.