Getting Serious About Software Testing: The Model-Based Approach

Agencies rely on software applications to deliver digital services reliably and effectively, regardless of resource constraints, security requirements, and other challenges. Traditionally, organizations tested their software manually — a slow, cumbersome and expensive approach, to be sure, and one that’s particularly ill-suited for AI and cloud environments and today’s fast-paced demands.  

While manual testing may never disappear, most agency testing should be automated — and model-based automation is the most efficient, nimble and forward-thinking option. It allows agencies to scale up or down without increasing headcount and empowers staff to create end-to-end, AI-driven test cases by dragging and dropping fields, rather than writing code. But agencies should take a measured approach when transitioning from more burdensome techniques to a model-based solution.

“You don’t go and say, ‘We’re going to automate all of our processes.’ You want to start with the ones that are really critical … and show to executive management that, ‘Hey, we can do this, and we feel confident that these processes are going to work,’” said John Lange, Principal Product Marketing Manager with Tricentis.

 In this video interview, Lange explains how model-based automated testing enhances efficiency and modernization. Topics include:  

• How model-based testing differs from other testing options
• Best practices for implementing a model-based approach
• How AI does, and will, influence software testing