Systems | Development | Analytics | API | Testing

Scaling test automation from hundreds to thousands of tests introduces challenges underestimate. Maintenance overhead compounds as UI changes ripple through test suites. Parallel execution becomes essential but complex to orchestrate. Enterprise applications like SAP, Salesforce, and Oracle demand specialized testing approaches.

AI is reshaping test automation fundamentals. Features that once required hours of manual scripting can now adapt automatically to UI changes, generate realistic test data on demand, and help teams predict which tests matter most. For QA engineers evaluating automation platforms, understanding how AI capabilities differ has become essential. This comparison examines SmartBear TestComplete, Tricentis Tosca, and Ranorex through their AI-powered features.

In November 2023, I flew to Germany to speak at Agile Testing Days, one of the leading software testing conferences. My keynote was called “10x Software Testing.” I knew there would be skepticism in the room.

Microsoft officially retired the built-in Salesforce ODBC connector in Access on October 28, 2025 for Microsoft 365 monthly versions and November 11 for semi-annual and perpetual licenses. Teams that relied on Access for quick Salesforce data pulls, lightweight reports, and dashboard connections suddenly found their Microsoft Access Salesforce connection not working. Here, we discuss what happened, who’s affected, and how to get your data flowing again with minimal disruption.

And instead of getting a generic answer or being told to check your dashboard, the AI pulls the exact numbers from your company’s data and gives you a real answer in seconds. This is no longer science fiction. A new technology called MCP (Model Context Protocol) makes this possible. It’s a standardized way for AI tools to securely connect to your business intelligence and analytics platforms and actually work with your real data.

Conversational Analytics in BigQuery in preview brings a sophisticated AI-powered reasoning engine directly into BigQuery Studio.

In the semiconductor world, “software" is more than just application code. It is a complex stack of firmware, bootloaders, microcode, drivers, and Board Support Packages (BSPs) that are intricately linked to the hardware being designed. To secure the supply chain, meet customer expectations, and maintain market access, semiconductor leaders need a dynamic, "living" SBOM strategy that assesses risk in real-time and provides a single source of truth for all teams to work from.

So as software engineers using AI coding assistants, we’re quickly learning of a new anti-pattern: Hallucinated Success. You give your agent (e.g. Claude via terminal or various IDE code assistants) the command “refactor the billing controller.” The agent happily complies, churning out nice clean code. The agent even goes so far as to write a new unit test suite that passes at 100%. You integrate it. Your test suites pass. Your production code breaks. Why?

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.