SmartBear Delivers AI-Enhancements Across Entire Software Application Testing Lifecycle

SmartBear, helping teams build, test, and ship quality software at AI speed and scale, announced AI enhancements for API testing, UI test automation, and test management across its product suite, the SmartBear Application Integrity Core™. These capabilities improve and accelerate application testing to match the increased speed and volume of AI-driven code creation.

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The new capabilities add agentic and AI firepower to human-led testing workflows – including leveraging AI for on-premise applications. They follow SmartBear’s recent release of BearQ™, its fully autonomous testing solution, to round out the industry’s most comprehensive portfolio of AI-infused application testing products. Enhancements include:

• New agentic capability in the SmartBear test automation platform, Reflect, that lets developers and QA engineers generate automated tests directly from their coding environment. By invoking Reflect through the SmartBear MCP server, teams can pull in richer context, drawing on existing test assets, unified visibility and reporting, and development history. This creates context-aware tests agentically and accelerates automation adoption without starting from scratch.
• New Rovo agent skills for Zephyr enable natural-language queries within Atlassian Jira to evaluate test coverage, search test executions, and assess release readiness, so QA teams can quickly identify gaps and prioritize testing work.
• AI capabilities to SmartBear’s on-prem tools for desktop testing and secure, local environments—including natural-language AI test generation in ReadyAPI for building complex multi-step API tests, and enhanced AI-based object detection in TestComplete. This will improve automation reliability for rapidly changing applications, all with enterprise governance controls to meet compliance and quality standards.

Together, the new capabilities mark the highest volume of AI features released at one time, underscoring SmartBear’s focus on scaling AI development across the testing lifecycle and the need to adapt to rapidly changing AI ecosystems.

“SmartBear is firing on all cylinders to enable QA teams to move faster and improve application level testing. We see some teams racing toward fully autonomous solutions like BearQ, and others deploying AI-enabled tools to complement human-directed automation or even manual workflows,” said Vineeta Puranik, SmartBear CPTO. “We meet customers where they are on their AI journeys by helping teams adopt AI confidently, scale testing effectively, and maintain application integrity as software delivery accelerates.”

SmartBear defines application integrity as continuous, measurable assurance that software works as intended, with governance to operate at AI speed and scale. Given the increasing speed of AI-driven code creation, and the risks associated with that code, new solutions are needed to ensure application testing keeps up. In the recent SmartBear Study: Closing the AI Software Quality Gap, 273 software testing and quality decision makers found that seven of 10 respondents are concerned that quality is already suffering as AI speeds code creation and 68% are worried that faster AI code development will create testing bottlenecks.

“Organizations are looking for practical ways to apply AI across their software delivery lifecycle,” said Chris Lewis, CEO of Praecipio, an Atlassian-specialized management consulting firm and SmartBear partner. “Capabilities like these from SmartBear help teams uncover testing gaps and act on them quickly, exactly the kind of innovation we help our clients operationalize.”

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