Jupyter Notebooks have gained immense popularity in data science and academia. Notebooks allow code that is editable and executable to live side by side with the resulting visualization and an explanation for its intent.
The same power and flexibility can be leveraged for test automation. By introducing Jupyter notebooks into test automation frameworks the full story of a test can come to life right in front of the testers eyes.
Learning hands on how using Jupyter Notebooks with test automation can:
Create TDD-esque feedback loop, allowing automation engineers to interactively code, inspect and explore. Quickly seeing the results of their code and correcting errors without breakpoints or the need to rerun the test to make changes.
Cede control to human testers for challenging controls or integrations. Semi automated tests can be created allow human interaction with the system under test at any point in a test case.
Integrate with Exploration. Testers can mix and match automation with manual steps, using different inputs and datasets then documenting their findings in markdown including links and screenshots.
