For production-grade Shiny applications, regression testing ensures that the application maintains its core functionality as new features are added to the app. With the help of various R and Python tools that programmatically interact with the UI and examine UI outputs, regression test logic can be represented programmatically and can run as often as needed. This gives the development team an opportunity to catch and fix bugs before they are pushed to production.
In this talk, I will introduce a framework for automated testing of Shiny applications both (1) during the development phase and (2) after the app is deployed. I will share a demo Shiny app along with relevant shinytest2 and Selenium code.
Dr. Sydeaka Watson earned a Ph.D. in Statistics from Baylor University and has several years of teaching experience. As a Research Assistant Professor in The University of Chicago Biostatistics Laboratory, she consulted with over 110 biomedical research teams, specializing in statistical analysis and experimental design for clinical research studies. In both of her Senior Data Scientist roles in the AT&T Chief Data Office and at Elicit Insights, she used predictive analytics and visual tools to implement custom solutions for customer behavior analytics and customer management. Now, as Senior Advisor in the Eli Lilly Statistical Innovation Center, Dr. Watson develops tools that enhance clinical trial design workflows using machine learning, big data technologies, and data visualization.
