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The Evolution From CAT To Autonomous Testing Platforms

The software testing landscape is undergoing a seismic shift. For years, Continuous Automation Testing (CAT) platforms have been the gold standard for reducing manual testing and ensuring comprehensive coverage across diverse environments. However, with the advent of generative AI (GenAI) and large language models (LLMs), we are entering a new.

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AI-Driven Software: Why a Strong CI/CD Foundation Is Essential

It is easy to get caught up in the excitement of technologies like AI and dive straight into experimentation without laying the right groundwork. The rise of AI-generated code and increasingly sophisticated AI agents will change the game for enterprises and startups seeking to do more with less. But it’s.

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How AI is Transforming Coding?

How AI Tools Are Changing the Way Developers Code Summary AI is changing the way developers write, test, and debug code. It saves time, improves quality, and opens up new ways to create software. With AI-powered tools, developers can focus on innovation and problem-solving, pushing the boundaries of what's possible.

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5 common assumptions in load testing—and why you should rethink them

Over the years, I’ve had countless conversations with performance engineers, DevOps teams, and CTOs, and I keep hearing the same assumptions about load testing. Some of them sound logical on the surface, but in reality, they often lead teams down the wrong path. Here are five of the biggest misconceptions I’ve come across—and.

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How to Move from Manual to Automated to Autonomous Testing

As organizations embrace AI-driven test automation to boost efficiency and reduce costs, success hinges on patience and strategic planning. It is not an understatement that software development and delivery are dramatically evolving thanks to AI's rapid emergence from a concept mainly found in science fiction movies to an important part of the everyday software.

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T&M Meets AI/ML: Transforming Testing and Measurement in the Age of Intelligence

In an era defined by digital transformation, innovation is reshaping every corner of the technology landscape. Among the most significant developments is the convergence of Testing and Measurement (T&M) with Artificial Intelligence (AI) and Machine Learning (ML). Traditionally, T&M processes have relied on rule-based systems and manual intervention. The transformation of testing and measurement in.

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Shift-Left Testing in Practice: Lessons from the Field

We’ve all heard the conversations around shift-left testing — the idea of pushing test execution earlier in the development lifecycle to catch issues sooner, reduce rework, and accelerate delivery. But what does it take to help bring that vision to life as part of a development and QA team? In.

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Quality assurance in the era of LLMs – Methodologies for evaluating and validating generative AI systems

With generative AI systems evolving quickly to change the landscape of software development, traditional testing methods are inappropriate for such complex, non-deterministic systems. Gartner says that by 2025, 80% of software companies will have AI-based test strategies, enabling these companies to be more reliable and efficient. With the generative AI.

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From Testing Hell to Quality Heaven With Intelligent Continuous Testing

In the heart of a fast-growing Fintech company, the Payments and Transfers business unit was facing a quiet crisis. Tasked with rapidly rolling out new capabilities while ensuring transactional reliability, they had a strong team, an ambitious roadmap and a market eager for innovation. Yet, with every release cycle, they.

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