Systems | Development | Analytics | API | Testing

Clicking links is one of the most basic things a user can do on a website. If you're writing test scripts, it’s also one of the most common actions you’ll need to automate. With Selenium WebDriver, you can interact with any hyperlink on the page, no matter if it's visible, hidden, dynamic, or opens in a new tab. All you need is the right locator and a clear understanding of how the browser responds.

Teams use different tools in their software testing life cycle. The problem? Each tool has its own way of communicating. The MCP (Model Context Protocol) Server is a new approach to integrating these tools. It’s like a universal translator, so your testing tools, scripts, and AI copilots can share context without endless plugins or one-off integrations.

BugSnag now supports applications built with the Vega Developer Tools from Amazon Devices & Services, giving developers immediate visibility into the stability of their TV-first apps. This launch is part of a deeper collaboration between SmartBear and Amazon, extending beyond tooling support into a broader partnership that accelerates software quality across the Amazon ecosystem.

With the rapid rise of AI, the emergence of the MCP protocol reshaping human-machine collaboration, and testing tools like OctoPerf making their mark in the DevOps landscape, we’re clearly riding a new tech wave… and it’s got style. I wanted to dive into this project because it felt both fun and challenging. It was the perfect opportunity to explore what AI, the MCP protocol, and OctoPerf could really offer… and to see how far we could push the possibilities.

Do you know that moment when your old on-prem systems creak under pressure? Apps are slow, updates are a nightmare, costs are ballooning, and innovation is bottlenecked by outdated infrastructure? Many organizations are waking up to the fact that keeping things as they are is no longer viable. Application migration to Azure isn’t just a tech upgrade; it’s a strategic move.

Traditional automation in software testing is beginning to show its limitations. Once regarded as the benchmark for speeding up QA, now struggles to keep pace with modern software development. Agile methodologies, DevOps practices, continuous delivery, and rapidly evolving user journeys require testing strategies that are more innovative, quicker, and adaptable.The challenge? Old automation frameworks still lean too much on people. They rely on fixed scripts, constant maintenance, and manual oversight.

For years, QA practices were designed for predictable, rules-based software. AI has upended that reality by introducing risks that traditional methods cannot fully address.