https://www.mdu.se/

Introduction: Software reuse by copying and modifying components to fit new systems is common in industrial settings. However, it can lead to multiple variants that complicate testing and maintenance. Therefore, it is beneficial to detect the variants in existing codebases to document or incorporate them into a systematic reuse process. For this purpose, model-based clone detection and variability management can be used. Unfortunately, current tools have too high computational complexity to process multiple Simulink models while finding commonalities and differences between them. Therefore, we explore a novel approach called MatAdd that aims to enable large-scale industrial codebases to be processed.

Objective: The primary objective is to process large-scale industrial Simulink codebases to detect the commonalities and differences between the models.

Context and method: The work was conducted in collaboration with Addiva and Alstom to detect variants in Alstom's codebase of Simulink models. Alstom has specific modeling guidelines and conventions that the developers follow. Therefore, we used an exploratory case study to change the research direction depending on Alstom's considerations.

Results and Conclusions: The results show that MatAdd can process large-scale industrial Simulink codebases and detect the commonalities and differences between its models. MatAdd processed Alstom's codebase that contained 157 Simulink models with 7820 blocks and 9627 lines in approximately 90 seconds and returned some type-1, type-2, and type-3 clones. However, current limitations cause some signals to be missed, and a more thorough evaluation is needed to assess its future potential. MatAdd's current state assists developers in finding clones to manually encapsulate into reusable library components or find variants to document to facilitate maintenance.