Observability refers to understanding a system’s internal state by examining its external outputs, like logs, metrics, and traces. Borrowed from control theory, it’s the ability to infer what’s happening inside a system without direct access. To visualize observability, consider the human body. We don’t need to open it up to understand its internal state. By measuring external signals like body temperature, heart rate, or blood pressure, we can infer what’s happening inside.

SmartBear is thrilled to announce our acquisition of QMetry, a leader in AI-enhanced test management solutions, into the family. This merger is not merely about expanding our already robust solutions—it’s about revolutionizing the delivery of software quality across industries. In today’s fast-paced tech landscape, addressing software quality issues is critical; they can significantly impact customer satisfaction and financial growth, while flattening the quality cost curve.

“Quality is more important than quantity. One home run is much better than two doubles.” – Steve Jobs In the world of software testing, this quote couldn’t be more relevant. If you’re a manual tester, you know the pressure to deliver high-quality results, often within tight deadlines and limited resources. Yet, with today’s fast-paced software development cycles, meeting these demands through manual testing alone is increasingly challenging.

They say with great power comes great responsibility. As GenAI and machine learning technologies gain prominence, the need for AI governance – structures and policies that ensure AI is used ethically, responsibly, and transparently – has become critical to long-term success. The stakes are high, and in today’s tech ecosystem, the absence of responsible AI governance could be a company’s undoing.

The world of quality assurance (QA) has changed, and there’s no turning back. Digital transformation is driving the need to produce high-quality software faster, while also keeping costs under control. The old idea of choosing between speed, cost-efficiency, or quality? That’s out the window. Today, you need all three to succeed, and they’ve got to work together.

AI is no longer just a buzzword – it’s the driving force behind the next wave of innovation in the software industry. Companies that embrace AI today are automating tasks, boosting efficiency, and unlocking new levels of productivity. However, as revolutionary as AI is, adopting it within technical software teams isn’t without its challenges. From skill shortages to navigating ethical dilemmas, businesses face a steep adoption curve.