{"id":197952,"date":"2026-02-17T09:06:42","date_gmt":"2026-02-17T09:06:42","guid":{"rendered":"https:\/\/innovationspace.ansys.com\/knowledge\/?post_type=topic&p=197952"},"modified":"2026-02-27T09:30:37","modified_gmt":"2026-02-27T09:30:37","slug":"testing-scade-one-models-with-python","status":"publish","type":"topic","link":"https:\/\/innovationspace.ansys.com\/knowledge\/forums\/topic\/testing-scade-one-models-with-python\/","title":{"rendered":"Testing Scade One models with Python"},"content":{"rendered":"

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Model-based development is evolving quickly, and Scade One <\/strong>represents the next generation of SCADE technology, designed to make embedded software modeling more intuitive, scalable, and accessible. While Scade One possesses a fully-integrated, model-based test offer<\/a>, it is also open to interfacing with other testing frameworks as required by user workflows. In particular, the PyScadeOne<\/a> library connects the product with the full Python ecosystem.<\/p>\n

In this article, we will explore how to leverage pytest<\/strong> to set up a highly efficient workflow for building, verifying, and maintaining high-assurance embedded software.<\/p>\n

PyScadeOne: the Python bridge to your models<\/h3>\n

At the heart of integrating Scade One models with Python is PyScadeOne<\/a>, a Python library that provides APIs to trigger IDE features. This includes browsing, navigating and editing Scade One models, launching jobs such as code generation and FMI 2.0 export, and encapsulating the generated code into a Python-accessible wrapper.<\/p>\n

This library is documented with examples and enables a smooth workflow that brings your model directly into Python for testing, simulation, and automation.<\/p>\n

PyScadeOne is published as free, open source software (MIT license) under the PyAnsys<\/a> initiative.<\/p>\n

Exporting Scade One models as Python-callable code<\/h3>\n

Using the PyScadeOne’s Python wrapper<\/a> functionality, you can export a Scade One model (in the Swan language) as a Python function<\/strong>. This process wraps C code generated by Scade One so it becomes directly callable from a Python script.<\/p>\n

Once this is done, you may use the rich Python ecosystem to write and run tests using pytest. Alternatively, you may also leverage Python libraries such as NumPy<\/strong>, SciPy<\/strong>, or Jupyter Notebooks<\/a> for simulation, analysis, and reporting.<\/p>\n

In other words, your model becomes a first-class citizen in the Python ecosystem.<\/p>\n

Enter pytest<\/h3>\n

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Pytest<\/a> is one of the most widely used Python testing frameworks, known for its simplicity and power. We will use it in this article, although the workflow we illustrate here would work with any Python-based testing framework.<\/p>\n

Pytest features a simple syntax<\/strong> using plain assert<\/code> keywords. Test discovery is automatic (test_*.py<\/code> files are tests by convention), and the library is extensible via plugins.<\/p>\n

For complex test setups, pytest provides rich fixtures<\/strong> as well as parameterization<\/strong> for running multiple test scenarios automatically.<\/p>\n

Finally, pytest can generate detailed HTML or JUnit reports<\/strong> and integrates easily with CI\/CD pipelines<\/strong>.<\/p>\n

When paired with PyScadeOne, pytest becomes a seamless testing layer over your Scade One models.<\/p>\n

Workflow overview<\/h3>\n

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The development and testing loop looks like this:<\/p>\n