Systems | Development | Analytics | API | Testing

XPath vs CSS Selectors in Katalon: Write Stable Locators

Robust test automation in Katalon Studio starts with stable test objects. Flaky tests almost always trace back to one root cause: brittle locators that break the moment the UI changes. The best approach is to use unique, static attributes like id or custom data-qa attributes. When those aren't available, CSS and XPath are your tools. This post covers how to write each type of selector, when to choose one over the other, and how to handle dynamic attributes using contains() and starts-with(). At a glance.

Ai-Powered Test Automation: A Complete Guide for Engineering Leaders

Your developers are shipping more code than ever. GitHub Copilot, Cursor, and tools like them have fundamentally changed developer throughput - some teams are seeing 40-76% more code per person per sprint. That is the headline everyone celebrates. The part that keeps engineering leaders up at night is the other side of that equation: your testing pipeline has not changed at the same pace. Tests that used to gate two releases a week now need to gate ten.

AI in Software Testing: The Triple Threat to QA in 2026

It is Monday morning. Your VP of Engineering just forwarded a company-wide memo: every team needs to demonstrate AI adoption by end of quarter. At the same time, you learned last week that your QA budget was trimmed by 15%, because leadership assumes AI will "make testing more efficient." And your developers? Thanks to Copilot, Cursor, and Claude Code, they are now shipping 76% more code per person than they were two years ago.

Many talk about bringing Al into testing - what makes Katalon stand out?

What makes Katalon stand out is its tester-first approach to AI. Instead of chasing flashy demos, Katalon has spent years co-developing AI capabilities with customers, focusing on how AI fits naturally into real testing workflows. The result is AI that testers can actually adopt and trust, delivering measurable gains in productivity, speed, and efficiency in day-to-day work — Alex Martins, VP of Strategy at Katalon.

Why does AI native development require AI native testing?

AI native development requires AI native testing because testing teams now face code generated not just by developers, but by AI agents as well. To keep pace and maintain quality, testers need comparable AI-powered capabilities that can generate, assist, and scale testing alongside AI-driven development, helping level the playing field and support faster, more efficient delivery — Coty Rosenblath, Chief Technology Officer at Katalon.

Did you expect the implementation to take longer than it actually did?

Yes, the team expected the implementation to take longer. Given the client’s secure environment, multiple pre-production setups, and many agile teams, the plan allowed for nine weeks or more. In reality, the implementation was completed in about six weeks, even with an unplanned pause, which exceeded expectations and highlighted how smoothly the rollout went — Mush Honda, Chief Quality Architect at Katalon.

What made the first TrueTest implementation successful, and what lessons or surprises came from it?

The first TrueTest implementation succeeded because the team aligned early on clear goals and maintained an open, realistic mindset about what AI could deliver. By being transparent, receptive to guidance, and working in a stable environment, they were able to move quickly and achieve measurable value much faster than expected, which was the biggest and most positive surprise from the implementation.

Al boosts developer speed, so why does it slow testers down?

AI slows testers down when it’s added without a tester-first experience. Testers naturally question coverage and intent, so Katalon designs AI around real testing workflows to boost productivity instead of creating friction. — Alex Martins, VP of Strategy at Katalon Follow Katalon for more insights in our series!

You've described Katalon as a hybrid testing platform. What does that really mean?

Calling Katalon a hybrid testing platform means it brings manual, automated, and AI-generated testing together in one unified system. Katalon is built to help teams design, run, manage, and report on all three types of tests side by side, giving clear visibility into coverage, quality, and release readiness without forcing teams to choose just one testing approach — Alex Martins, VP of Strategy at Katalon.