Systems | Development | Analytics | API | Testing

Mobile application users expect flawless experiences on every device, every OS version, and every screen size, and they have little patience for anything less. Yet for QA teams, achieving that level of coverage traditionally means wrestling with brittle automation scripts, complex Appium setups, and endless device fragmentation. Even after all this manual effort, your mobile app quality could contain unseen gaps.

An automation suite that’s losing ground rarely makes it obvious. Coverage numbers look reasonable. Tests are running. The CI pipeline is green more often than not. Meanwhile, the team is quietly working around what isn’t working – rerunning tests until they pass, deferring maintenance, or accepting a regression window that’s wider than it should be. Those workarounds can feel normal. They aren’t.

Automated test suites tend to follow the same arc. The suite works well until the application changes and a block of tests fails. Someone fixes them. The application changes again. At some point, the work of keeping tests current starts consuming the time that should go toward coverage decisions, risk assessment, and the testing work that requires human judgment.

Not every test automation problem looks the same. A team maintaining complex desktop applications in a controlled financial services environment has different automation needs than a team shipping web and mobile updates every two weeks. The application, the environment, and the people creating tests all shape what “good automation” has to do.

For teams building APIs as products, the developer portal is the front door to your business. It’s where partners discover your endpoints, where customers authenticate and start integrating, and where the quality of the experience directly shapes adoption. Now that changes.

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.

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.

For everyone in QA inside the Atlassian ecosystem, the last few months have made one shift obvious. AI is changing how software gets built, and the quality function must change with it.

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.

While AI accelerates development velocity by a factor of ten, a critical consequence remains: testing hasn’t kept pace. According to SmartBear research, 70% of software professionals report that their application quality has already degraded due to AI-accelerated development. Even more concerning, 60% have experienced quality issues in the past year as development velocity outstrips testing capacity.