Systems | Development | Analytics | API | Testing

AI is changing how testing gets done. As automation grows, so does the complexity of tracking what’s been tested, what passed, and what’s ready to release. See how SmartBear Reflect and QMetry work together to scale AI-powered test automation without losing visibility or control. Reflect makes it easy to create and run automated tests using plain language, while QMetry brings structure to that speed, connecting tests, results, and reporting into a single system of record.

SmartBear’s study Closing the AI software quality gap found that 60% of teams have already experienced quality issues tied to AI-generated code, evidence of how increased abstraction is changing how software gets built. When development shifts from well-defined requirements to prompts and generated outputs, it becomes much harder to understand what the system is actually supposed to do, and what you should be testing against.

Release day shouldn't mean chasing answers across Jira. SmartBear Zephyr is the Jira-native testing system of record that empowers your team to deliver better software, faster. In this demo, see how Zephyr Skills for Rovo bring test management and automation insights directly into Jira. Connect planning, testing, and delivery in a single, unified workflow within the Atlassian system of work so your team can make faster, more confident release decisions.

The tsunami of AI-generated code creates downstream bottlenecks for QA teams, and shift-left or traditional test automation aren't enough in the AI era. In this From the Bear Cave session, SmartBear CEO Dan Faulkner and CMO Kelly Wenzel unpack how AI code generation impacts software quality and why traditional testing struggles to keep up.

Testing complex UI elements like CAD software, Google Maps, or Citrix environments often leads to brittle tests and false negatives. Vision AI solves these automated testing challenges by recognizing elements just like a human would, reducing manual testing efforts, and improving accuracy. Discover how vision AI strengthens automated testing for visually complex applications. This tutorial shows you how to enhance object recognition in SmartBear TestComplete and eliminate test failures caused by 3D applications, canvas-based apps, and virtualized environments.

Recent SmartBear research shows that 70% of teams are already seeing quality degrade with AI-generated code, creating a real bottleneck in the software-development lifecycle (SDLC). As output increases, QA teams are left choosing between delaying releases to validate changes or shipping faster with less confidence in what’s actually working. In this From the Bear Cave clip, SmartBear CEO Dan Faulkner and CMO Kelly Wenzel dig into a growing gap in modern software development: how AI is accelerating code generation but testing and quality validation aren’t scaling with it.

AI-generated code is flooding your pipelines. Your test automation debt is piling up. If this sounds familiar, you're not alone. Velocity can't come at the cost of quality. As AI transforms how we build software, API testing must evolve. Join Justin Collier, Senior Director, Product Management, and Yousaf Nabi, Developer Advocate, to explore the future of API testing in an AI-driven world.