By Kevin Surace  | AI, Generative AI, Predictions, Scription

Generative AI is Here in Testing

Generative AI is a type of artificial intelligence (AI), one of many, where it is trained on a very large set of data. After training, if you give it some direction, it generates something for you.

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By Ken Miralles  | Test Strategy | Video Streaming Applications |  Autonomous Testing

By Kevin Parker  |  Autonomous Testing

Unlocking Seamless Testing: A Guide to Choosing Your Autonomous Testing Partner

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DevOps practices have emerged as a solution to streamline and accelerate the development lifecycle, bridging the gap between development and operations. At the heart of successful DevOps implementation is continuous testing, a practice that ensures every code change is automatically tested to maintain quality throughout the development process. Let’s delve into how continuous testing fits

In the dynamic realm of software development, ensuring high-quality products is paramount. Traditional testing methodologies, where testing is primarily performed towards the end of the development cycle, often lead to the discovery of critical bugs late in the process. This can result in prolonged timelines, increased costs, and compromised product quality. Enter the shift-left testing

AI and machine learning (ML) are without a doubt revolutionizing various processes, and software testing. Traditionally known for being labor-intensive and time-consuming, software testing is undergoing a transformation, becoming more efficient and accurate thanks to AI and ML. One standout in this field is Appvance, a company leveraging these advanced technologies to automate test case

In today’s digital age, video streaming has become an integral part of our entertainment landscape, with platforms like Netflix, Disney+, HBO Max, and many others offering a vast array of content at our fingertips. Behind the seamless user experience lies a complex web of technology and infrastructure, presenting unique challenges for testers striving to ensure

As technology advances at an exponential rate, the role of artificial intelligence (AI) in software quality assurance (SQA) has become increasingly prominent. From automating mundane tasks to enhancing overall efficiency and productivity, AI has proven itself as a powerful tool in the arsenal of QA teams. But what does the future hold? Can AI go

In the ever-evolving landscape of software development, the demand for rapid, efficient, and reliable testing processes has become paramount. As traditional methods struggle to keep pace with the dynamic nature of modern applications, autonomous testing has emerged as a beacon of hope. But navigating the realm of autonomous testing requires careful consideration, especially when selecting