Systems | Development | Analytics | API | Testing

Creating test reports is an essential part of software testing, but manually compiling information from Jira can quickly become repetitive and time consuming. Whether you're preparing evidence for an audit, sharing release progress with customers, or documenting test coverage, reporting should help your team, not slow it down.

May 2026 marks a turning point for how QA teams think about automation. This month's releases push Katalon True Platform closer to a world where you describe what you want to test, and the platform handles the rest: from generating test cases to running them, then handing you a ready-to-use script. Here is everything that shipped in May.

MCP servers have quietly become production infrastructure. If you have shipped a Model Context Protocol server so that Claude, an agent, or a connector can call your tools, that endpoint is now on the critical path. When it goes down, or starts returning errors, your users feel it the same way they would feel any API outage. The difference is that most teams are not watching it yet.

Enterprise QA teams are discovering that deploying machine learning models breaks their existing validation pipelines. Legacy testing environments rely on a simple truth: fixed inputs must produce predictable outputs. Because intelligent architectures operate on probabilistic distributions, deterministic testing alone can no longer guarantee reliability. When conducting a code review or architectural risk assessment, treating an active model as a standard black-box API leaves critical flaws unaddressed.

This fall, Tosca’s suite of AI capabilities will be available for on-premises deployments — giving teams the tools to meet the pressure of the AI era head-on. Customers will gain access to leading AI features, including Agentic Test Automation, AI-powered test creation, MCP connectivity, and AI Workspace, no matter their deployment type or existing infrastructure. AI’s unprecedented development means testing practices that were standard a year ago could already be outdated.

Picture a software testing failure at your company. The consequences range from money lost on weeks of rework to life-threatening catastrophe. These are the errors that keep engineers, their directors, and executives up at night. At some point, those risks came to life in the real world and pushed software teams to develop rigorous testing methodologies. But for some reason, we don’t think about data the same way. We think of it as reliable, durable, and highly unlikely to cause a big issue.