Systems | Development | Analytics | API | Testing

Recently, I had the pleasure of sitting down with Cristiano Caetano, VP of Product at Katalon, to explore his journey in software testing. It was a path that began with frustration, evolved through innovation, and now looks toward an exciting AI-driven future. Cristiano's insights shed light not only on his personal career path but also on the broader evolution and future of software testing.

There’s a particular kind of exhaustion that comes with traditional performance testing. You spend weeks building perfect load scenarios, run them overnight and wake up to a wall of red in your monitoring dashboard. Half your day disappears into log files, trying to piece together what went wrong. And just when you think you’ve got it right, a minor UI update breaks everything and you’re back to square one. If this sounds familiar, you’re not alone.

Coding with artificial intelligence is not just a nice-to-have; AI applications in computer programming are becoming integral to modern computer programming workflows. Presently, two primary applications dominate the discussions in this area: GitHub Copilot and Cursor AI. While both applications provide faster coding times and fewer bugs, fewer bugs, and smarter code, they offer such features in extremely different ways.

For gaming studios, the traditional manual testing and player beta period remains part of QA, but its limitations in the face of modern gaming’s scale and complexity can threaten a brand’s very existence.

For gaming studios, the traditional manual testing and player beta period remains part of QA, but its limitations in the face of modern gaming’s scale and complexity can threaten a brand’s very existence.

Every experienced developer has shipped code that worked during testing but later failed in production because critical execution paths were never tested. This is exactly why test coverage tools exist to uncover untested code, missed branches, and risky logic that traditional testing often overlooks. Without reliable test coverage, teams are forced to rely on assumptions rather than measurable evidence.