When people hear the name Gannett they often think about our national brand, USA TODAY. But we also have 109 local media organizations across the United States.

Each month more than 110 million unique visitors access content from USA TODAY and Gannett’s local media organizations, making us the largest U.S. newspaper publisher by total daily circulation.
Dealing with that many brands makes building, testing, and deploying our applications a massive undertaking. I also oversee 30 teams and one of the biggest challenges is that we don’t have a lot of QA resources.

With the manual tests we were running before, something would always go wrong during testing. Errors quickly added up and caused delays in our releases. It was taking weeks to release to production instead of the days or even hours it should have.

Our goal is to try to align our automation technology stacks with the application technology stacks so the developers can own some of that responsibility of quality. I knew that in order to achieve that quality and scale, we’d need to automate our testing. 

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By Greg Sypolt

You’ve heard of Artificial Intelligence (AI). The term has been around since Allen Newell, Herbert A. Simon, and Cliff Shaw wrote the Logic Theorist in the 1950s.

Historically, it’s safe to say you haven’t often heard AI and test automation discussed in tandem. But that is changing. AI testing automation is poised to play an increasingly important role in the future of automated testing.

AI test automation is still a relatively new concept to me, but it’s also one that I am exploring eagerly as I work to stay at the fore of the automated testing field. In this article, I want to take the opportunity to highlight why AI testing is so important, explain how AI bots can be used in automated testing, and discuss some of the challenges that we still need to solve in order to make the most of AI testing.

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