Continuous Modeling with MATLAB and Microsoft Azure DevOps

Is the concept of continuous integration familiar to you? Have you tried, or are you planning, to apply these practices to Model-Based Design? We want to show you how you can succeed at that tough task and present to you our approach to continuous modeling.

The complexity and challenges of any kind of projects, where a piece of code is needed, are getting bigger and bigger. This also applies to Model-Based Design, which is now used in almost every industry. Development and maintenance of models is not a single-player problem anymore. In contrast, development teams, usually located at different locations, even in different countries, are connected remotely (especially due to the COVID-19 pandemic) and work on different parts of bigger models.

Agile methodologies have come to stay and this is an overhead that development teams or companies adopting them from scratch must overcome. The adoption process of some of the most well-known agile methodologies is often underestimated, since the teams that are adopting them must be appropriately coached, new roles must be defined within the organization depending on the methodology, and so on. Choosing the right tool, in our case Microsoft Azure^® DevOps, has been crucial for success. All of that increases the challenges of the efficient model-based development.

Do the following points sound familiar to you?

• People sharing parts of models via USB flash drive, emails, or shared network folders
• Big bangs when models are put together
• Models are developed without any kind of version control system, e.g., git
• Unmaintainable and unversioned build scripts
• Lack of continuous integration strategy for models
• Lack of release and defect management for models

In this case, we want to show you our approach to continuous modeling to solve all these challenges in an elegant way. To this end, we have defined a modeling framework on top of Microsoft Azure DevOps. This framework allows us to create, maintain, and extend our existing models faster and more easily than before.