
Discover Enterprise Test Data®
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Discover EVERFI's 9-month journey to test generation in minutes with the power of Curiosity's platform.
EVERFI required scalable course automation, capable of testing vast permutations at speed, across devices, and with visual testing built-in.
With model-based testing, EVERFI's CI/CD pipeline auto-generates targeted tests within minutes of a check-in.
In just 9-months EVERFI reduced the time to create and run tests from weeks to minutes, with 100% permutation coverage.
To match the pace and complexity of course development, QE embarked on a digital transformation project to remove manual testing from 100+ courses. The project sought to scale rigorous test automation across the whole organisation.
Before the introduction of a Quality Engineering (QE) mindset, course testing at EVERFI was made up wholly of manual testing. However, this was too time-consuming and costly. Suboptimal testing further risked hitting the same logic repeatedly, exposing EVERFI’s eLearning platform to bugs, while there was no way of communicating clearly what was being tested to developers or product owners.
Course automation required a scalable approach to creating tests, capable of testing the vast permutations created by course creation. The Quality Engineering team decided that hand-scripting every test would be too slow to test the modularised system architecture. They sought a rapid and automated approach to reusing the high-quality Cypress automation, avoiding the manual effort of copying, pasting and editing boilerplate code.
Our platform generates visual test assertions to validate how users see and experience EVERFI’s courses. This is as simple as adding a blue automation waypoint into a reusable model. A step in the generated Cypress tests then makes an API call to Applitools Eyes, all built in visual models.
With Curiosity's platform, the Quality Engineering team introduced targeted test generation, further optimising and accelerating course test automation. The targeted test generation is based on GitHub check-ins, focusing testing on impacted pages or modules, instead of re-running end-to-end tests.
Visual models break our system down in reusable chunks that generate the functional and visual tests we need. We can further assemble these reusable building blocks automatically as new courses are checked in, generating rigorous tests.
Greg Sypolt
VP of Quality Engineering
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