Systems | Development | Analytics | API | Testing

We are excited to announce the launch of Tricentis qTest Copilot, a generative AI assistant that helps save significant time in the creation of manual test cases. qTest Copilot is seamlessly embedded into the Tricentis qTest user interface as an expansion of our unified test management platform.

It’s often said that being first to market with a new product or idea is the key to success, and in many cases it is. But Apple has never chased the so-called first-mover advantage. Apple wasn't the first to release an MP3 player or even the first on the market with a smartphone, yet it has consistently redefined what's possible with technology.

Choreo is a powerful internal developer platform (IDP) as a service built for enterprises, offering a comprehensive suite of tools for CI/CD, API management, DevOps, observability, and business insights. It provides an all-in-one solution for building, deploying, governing, and monitoring enterprise applications—eliminating the need for developers to build complex CI/CD workflows or set up custom platforms.

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.

When it comes to building AI models, the process is often oversimplified as “just get a GPU and start building.” While securing access to GPUs can be a challenge, gaining access to GPU clusters is only the beginning of the journey. The real complexity lies in effectively leveraging GPU capabilities to deliver meaningful business impact.

The barrier to entry for building practical generative AI is lower than you think, but you have to follow the right steps.

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.

Combining Artificial Intelligence and human intelligence in testing becameessential for delivering high-quality products quickly and efficiently. AI excels at automating repetitive tasks, analyzing vast datasets, and improving test coverage. Humans, on the other hand, bring creativity, critical thinking, and the ability to handle complex scenarios that machines can’t easily navigate. Together, they form a powerful synergy that enhances speed and accuracy in testing but also brings challenges..

Part 1 of this two-part blog serves as the foundation, highlighting the critical role that quality engineering (QE) must play in addressing the business risks associated with the accelerating adoption of artificial intelligence or AI-powered software.

‍ They started in awe, which soon turned into desperation to keep up, and it is only now that we have started realizing the utility and business value of our Artificial Intelligence (AI) goals. I like this stage of our industrial revolution, where we are no longer expecting magic from AI but are integrating it nevertheless for all the wonders it can still do for our businesses. This was the same space where our DevOps efforts started yielding enterprise-level transformations.