Systems | Development | Analytics | API | Testing

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.

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.

Testing is a very important and necessary step in the SDLC, but most teams ignore it or don’t care much about it, while some teams spend most of their time on testing instead of building features. AI is really changing the way we write code, but most people use it mainly for writing test cases, and we still end up doing it manually. So in this blog, let’s see what AI testing is, how AI helps in testing our software, what AI tools are available, and which tools help with which part of testing.

‍ Healthcare isn’t short on data; it’s short on usable insight. Hospitals generate an enormous share of global data, and about 30% of the world’s data now comes from healthcare, growing at one of the fastest rates across industries (RBC Capital Markets). Yet an estimated 97% of health data still goes unused for care and operations (World Economic Forum).

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.

Have you ever considered that even with intensive software testing and effort, important software bugs can still get through? The issue, in most cases, isn’t that you didn’t put in enough effort, but that testers evidently lack the knowledge of a proper process to follow. A test script in software testing provides very precise instructions, step by step, to manage tester consistency, use proper test data, and confirm the expected results.