Systems | Development | Analytics | API | Testing

Browser automation with JavaScript is more powerful than ever. And if you're just starting out, using the selenium webdriver npm package is one of the most flexible ways to write and run your own automated browser scripts. Whether you're testing a login flow or building a web scraper, Selenium WebDriver helps you take control of the browser automatically. Add in NPM, and now you have a fast setup, easy dependency management and the full support of the Node.js ecosystem.

Before any software goes live, it must pass its final checkpoint: User Acceptance Testing (UAT). This stage validates the product against real business goals and user expectations, ensuring it’s not just technically correct but also usable in real workflows. In this blog, we’ll explain what UAT is, why it matters, and how to perform UAT testing effectively. Did you know nearly 70% of software projects fail because they don’t meet user needs – not because of coding errors?

In Selenium, interacting with a page element only works if that element exists. That's why checking whether an element is present is one of the first things any test should do. It confirms that the application has loaded the expected content and is ready for the next step. When you verify element present in Selenium, you reduce flaky failures and improve test reliability. This is especially helpful when testing dynamic apps that load content based on user actions or network timing.

You’ve likely heard a lot about MCP (Model Context Protocol) lately. It’s one of the many AI functions that are being added into the testing landscape every day. The open-source standard for connecting AI applications to external systems has been getting significant buzz in enterprise software testing, with Tricentis leading the way with its industry-first secure remote MCP servers.

The promise of AI breaks down when testing focuses only on idealized inputs. Real users ask incomplete questions, switch languages mid-thread, or provide contradictory details that models must still handle.

Autonomous agents — AI-driven bots, virtual assistants, and task-specific automators — are quickly becoming part of everyday business operations. They promise speed, efficiency, and the ability to scale tasks across teams without human intervention. But there’s a new challenge emerging in this AI-driven workplace: AI agent sprawl.