Systems | Development | Analytics | API | Testing

‍ Picture this: you finish your morning run, your smartwatch buzzes, and within seconds your phone shows your heart rate, pace, calories, and recovery time, all in one dashboard. That smooth, data-rich experience? It’s powered by apps like Garmin Connect, which have redefined how millions track and improve their fitness.

Automation has always been at the heart of efficient testing. It speeds up validation, eliminates repetitive steps, and helps teams catch issues earlier. However, even with great automation frameworks in place, one challenge persists: how to quickly and accurately convert manual tests into automated scripts? For most teams, this process still requires time, technical skill, and lots of rewriting.

In today's hyper-competitive digital world, software is no longer a basic, single thing. We live in a world with distributed architectures, microservices, cloud-native environments, and complicated API ecosystems. This complexity has changed how we produce things, but it has also broken down conventional, walled-off ways of ensuring quality, pushing the boundaries of traditional software testing services.

TL;DR: Claude Code writes features fast, but you need integration tests. With proxymock MCP, Claude Code can pull real production traffic and validate your changes automatically—one prompt, no manual setup. Watch it catch bugs before production.

Every year, KubeCon offers a candid look at where the cloud-native community stands — the tools gaining traction, the pain points teams share, and the big gaps still holding organizations back. After a week of deep conversations, session hopping, and talking to dozens of platform teams, one theme became impossible to ignore: Platform engineering still isn’t doing enough testing. And even more surprising: many teams don’t think testing is their responsibility.

When we first published this report last year, AI in software testing was still an experiment for most teams. Since then, well… everything has changed. In 2025, AI hasn’t been a promise of what’s to come — it’s become an everyday reality.

It’s a common idea across the tech industry: AI is fueling a new wave of technological innovation. Test automation is no exception. Over three decades, enterprise QA customers have turned to Tricentis Tosca to transform inefficient testing practices with model-based automation. Now, the company you’ve relied on for scalable, codeless test automation is entering a new era of agentic testing with Tosca’s cloud deployment as its foundation.

‍ Healthcare isn’t broken because of a lack of technology. It’s broken because most of the tech running hospitals today is outdated, overloaded, or barely held together with patches. Talk to any healthcare CIO and you’ll hear something like: “Our EHR is older than half our staff, the vendor sunset half the modules, and our IT team hasn’t slept since last quarter.” This isn’t drama, it’s reality.

‍ Healthcare’s most significant bottleneck in 2025 isn’t technology, it’s people. Across hospitals and clinics, staff shortages, burnout, and scheduling chaos are threatening the quality of care and driving operational costs upward. The U.S. alone is projected to face a shortfall of up to 3.2 million healthcare workers by 2026, according to Mercer.

If you’ve worked in QA long enough, you’ve seen how much testing has changed inside Jira. What started as a mix of spreadsheets, manual checklists, and endless review cycles has grown into fully integrated Test Management workflows. But even with automation, some challenges never went away. Writing test cases from requirements still takes time.