Systems | Development | Analytics | API | Testing

The pull request looked perfect. Two approvals, clean diff, all checks green. The team shipped it Thursday afternoon. By Friday morning, support tickets were coming in. On a popular mid-range Android device, the new checkout button rendered behind a promotional banner: visually present but physically untappable. Every reviewer read that code, yet none of them could have caught it, because the defect was never visible in the diff. It only ever appeared on the device.

An outdated Selenium or Appium build can look exactly like a flaky test … until you check the version number.

Many teams assume that if an API passes functional tests, it’s ready for production. Functional testing, however, only verifies that endpoints return correct results for individual requests. It does not reveal how the API behaves when subjected to heavy traffic or sustained load. Under real-world conditions – such as a surge of users logging in simultaneously – APIs that pass functional checks may still experience latency, errors, or outages.

The first payment is often the easiest part of the subscription lifecycle. A customer chooses a plan, enters a payment method, completes the transaction, and gains access. For the product team, that successful sign-up can feel like proof that the payment experience works.

Every release brings new capabilities to help QA teams work more efficiently, improve collaboration, and adapt to the evolving demands of software testing. The Xray Summer '26 Product Release Webinar introduced the latest innovations across the platform, from AI-powered capabilities to usability improvements and a preview of what's coming next. Whether you joined the live session or are catching up now, this article highlights the key announcements from the webinar.

Sovereignty is no longer a compliance debate. It is the operating condition for running data and AI in production. 89% of organizations in our 2026 survey of 320 enterprises rate data sovereignty as very or rather important. Only 38% have governance mature enough to survive a real AI production incident. That is the gap. It sits exactly where data and AI architecture meet enterprise control, and it is the central tension of the year.

When a build fails, the tests are the easy part. Here's how to implement AI root cause analysis so engineers stop losing hours to logs and diffs.

Selecting load testing tools is rarely a matter of picking the most feature-rich option. Instead, it’s about finding the right fit for your team’s skills, workflows, and performance goals. Open source solutions act like a Swiss Army knife: versatile, adaptable, and packed with options for those who know how to use them. Commercial platforms, by contrast, function more like a precision instrument: focused, reliable, and designed to deliver results with minimal friction.