Scale your Testing and Quality with Automation Engineering and ML
Many teams and organizations struggle to scale their quality and testing strategies once they reach tens of teams and hundreds of developers and services across their systems. Traditional strategies and techniques, like testing phases and code freezes, do not work at scale and quickly add friction, reduce productivity, and make testing and quality harder. In this presentation, we will cover different ideas and strategies to make things like BDD and TDD easier to adopt at the beginning, how to include observability and operability in your definition of quality, and how leveraging ML/AI can augment your devs and testers and reduce risk while accelerating value. By the end, you will have some "low quality" indicators that you can use to identify patterns and practices that won't scale well. You will have new insights and ideas for how you can set up your teams and strategies for success long term, and you will see tangible, practical examples you can take to your team and company to start this transformation now.
Session Information
Time: 3:05 - 4:05
Room: Great Hall 1 & 2 (1st Floor)
Carlos Kidman
Carlos Kidman
Carlos Kidman is a Director of Engineering and Test Architect, but was formerly an Engineering Manager at Adobe. He is also an instructor at Test Automation University with courses around architecture, design, and containerization. He is the founder of QA at the Point, which is the Testing and Quality Community in Utah, and does consulting, workshops, and speaking events all over the world. He streams programming and other tech topics on Twitch, has a YouTube channel, builds open source software like Pylenium and PyClinic, and is an ML/AI practitioner. He loves futbol, anime, gaming, and spending time with his wife and kids.