Systems | Development | Analytics | API | Testing

After a year of breakthrough innovation in 2025, Tricentis NeoLoad is headed into 2026 with even bigger goals: more intelligence, more automation, and more speed for performance testing at scale. In the coming year, NeoLoad will continue to provide the advanced foundational features to support effective performance engineering practices as well as intelligent workflows that enable quality and performance teams to work more efficiently than ever before.

When Oracle updates hit, many IT teams brace for impact. Backlogs swell, manual checks slow releases, and a patch that should take hours can stretch into days. For enterprise teams running Oracle at scale, outdated testing tools can be inefficient, costly, and difficult to manage. At Oracle AI World, two global organizations shared the stories of how they moved past those bottlenecks.

AI’s impact on quality engineering has been widely discussed, with some predicting a crisis for software testers. The more dire forecasts have narrowed in on the junior tester, as some anticipate that AI’s ability to perform routine tasks will eliminate entry-level roles. As Tricentis has explored, AI will not replace junior testers but will rather remake their jobs, enabling them to engage in strategic work earlier in their careers. But what about the senior tester?

AI code generation is rapidly evolving from a novelty into a key building block of modern software development. According to the Tricentis 2025 Quality Transformation Report, 82% of software professionals are excited about AI agents handling repetitive development tasks, and 84% believe AI will help teams meet increasingly compressed deadlines. Tools like GitHub Copilot and Codex are driving this revolution, offering real-time suggestions and automating boilerplate work.

If you find that your team is struggling to get releases out the door, it could be inefficient testing practices. Oftentimes, software teams don’t know what their tests actually cover, or which tests are relevant after each code change — so they run everything. This means spending hours executing full test suites for minor updates or burning through CI/CD resources while bugs slip through untested paths. On top of this, software is always becoming more complex.

Picture this: You’re creating test cases for a new feature. You have a Jira ticket with text requirements, a Figma mockup from design, a workflow diagram from the architect, and a screenshot from a stakeholder meeting. Traditionally, you’d manually translate all of this into test steps: describing the UI in words, interpreting the diagram, cross-referencing the mockup. But what if your testing tool could “see and “understand” all these artifacts directly, just like you do?