AI-Driven API Test and Automation in Action

APIs are an important part of systems. They store key business rules and have the advantage of being built in a fragmented way, allowing for truly early testing. Bugs in APIs tend to be critical because they affect many interfaces (whether other APIs or even UIs). Given this criticality, we need to develop truly effective tests and use truly efficient approaches. But how?

By leveraging all our technical testing knowledge and employing traditional test design techniques, exploratory testing, and, of course, functional and non-functional test automation. This knowledge is so important that without it, it would be nearly impossible to justify the tests performed. A tester with this knowledge can use GenAI responsibly to accelerate their work in each of these activities, especially with code editors with built-in GenAI at their disposal.

I’ve been using these technologies in my projects, and with them, I’ve been able to significantly accelerate my work and that of my team by improving API test design, automating functional and non-functional tests, and executing all of this in pipelines.

In this hands-on tutorial, participants will learn the technical concepts of API architecture (controllers, services, models, API specs, etc.), test design, test automation, and pipeline configuration. I’ll then guide them through how to use GenAI to implement each of these activities and increase productivity. This hands-on tutorial will begin by building a simple API using GenAI, running it, designing tests, automating them, and deploying them in a pipeline.

At the end, each participant will have a brand new repository with API code and test automation running continuously. Technologies we will use during the tutorial include: VS Code, Copilot, Swagger, Javascript, Mocha, Chai, Supertest, K6, Git, Github, and Github Actions.