There’s a situation that repeats itself across many QA teams: dashboards are packed with numbers, reports go out every week, and yet nobody can clearly answer whether the tests are actually working. The data is there, the problem is that nobody really knows what to do with it. QA metrics don’t exist to fill up

There’s a scenario that repeats itself across many QA teams: the test suite has grown over time, reaching hundreds, sometimes thousands, of test cases, and suddenly the pipeline starts taking hours to run. Failures show up, but when you dig into them, a large portion is flagging the exact same problem across different tests. The

If you work in software development or quality assurance, you’ve probably noticed that the conversation around AI has shifted over the past two years. We’ve moved from “this will transform everything” to “ok, but how does it actually work in practice?” 2026 is the year when teams that bet early on AI testing tools start

Every development team tests software. The question is whether they do it strategically, knowing exactly which type of test to apply at each stage. There are dozens of types of tests, and choosing the wrong one at the wrong time has a real cost, lost development time, expensive rework, or in the worst case, production

If you work with test automation, you’ve almost certainly crossed paths with Selenium. Launched in 2004, it was for years the near-unanimous reference in the industry: open source, support for multiple browsers and languages, enormous community. Few tools have left such a deep mark on the daily routine of QA professionals. But the market didn’t