Systems | Development | Analytics | API | Testing

Automatically catch API drift before your users do | Swagger Contract Testing

our API didn't break – it just stopped matching its contract. API drift is one of the sneakiest problems in modern API development. Your OpenAPI definition says one thing, your running implementation does another, and nobody notices until a consumer integration fails or a user hits an unexpected error. The longer it goes undetected, the harder it is to trace back to the source.

First look: Agents are coming to your favorite SmartBear products

AI is accelerating how teams build and ship software, but validating quality is getting harder, not easier. More AI-generated code means more to test, more API drift to catch, and more documentation that falls behind. The work is growing faster than teams can keep up.

The best tools don't force teams to change how they work

They fit into the workflows, processes, and environments teams already have. As Chris Armstrong, Manager of Developer Relations at SmartBear, explains, every organization is on a different stage of its journey. Some are exploring AI. Others are scaling it. Many are managing a mix of legacy systems, modern platforms, and everything in between. What teams need isn't another platform that demands a complete overhaul. They need solutions that respect their context while helping them move forward with confidence.

Building Confidence Across APIs and AI Agents with the Swagger Contract Testing Kiro Power

There is a specific kind of confidence that comes with deploying software. Not just “the tests passed” confidence, but the kind that comes from knowing the services your application depends on still behave the way you expect them to. Preserving that integrity becomes harder as systems grow, teams move faster, and AI agents become active participants in delivery workflows.

QMetry walkthrough: Test management, automation & AI-powered testing

Discover how QMetry streamlines test management with this comprehensive walkthrough covering requirements management, test design, test execution, automation, reporting, and AI-powered productivity features. Struggling with disconnected testing processes, manual test execution, and limited visibility into quality across your software development lifecycle? This complete QMetry tutorial shows testers, QA teams, and engineering organizations how to manage the entire testing workflow in a single platform, from requirements to release.

SmartBear at Atlassian Team '26: A Recap of What's New with AI and Rovo

What did Atlassian Team ’26 reveal about the future of software quality and AI-powered delivery? In this recap from the event floor inside the Anaheim Convention Center, SmartBear shares key themes from the event, including Atlassian Rovo, the Teamwork Graph, AI-driven workflows, and how QA teams are adapting to faster, AI-assisted software delivery inside Jira. See quick highlights from the event floor, SmartBear’s latest Zephyr innovations, and how conversational AI and quality intelligence are becoming part of the modern software delivery workflow.

How to scale AI test automation without losing test visibility

According to SmartBear’s Closing the AI Software Quality Gap study, 93% of teams are already using AI to generate code. The same study found that 60% expect AI to produce nearly half of all code within the next year. This shift in development velocity is already impacting software testing and quality. Most teams say application quality is suffering, and 60% have experienced quality issues in the past year because development is moving faster than testing can keep up.