Ensuring the reliability, functionality, and overall quality of software applications has become increasingly crucial. Quality assurance plays a vital role in achieving these objectives by implementing systematic processes and techniques to evaluate and enhance software quality. As technology continues to advance at a rapid pace, new and innovative approaches are emerging to tackle the challenges of software quality.

At ThoughtSpot, we believe making data accessible to every knowledge worker requires human-centered technology—an analytics experience that bridges the “language” barrier between technology and people. AI is the perfect compliment to search because it empowers organizations to analyze, understand, and act on data.

Mobile apps are essential for many businesses to reach their target customer. As a result, flawless performance has become a top priority. Mobile testing plays a crucial role in this context by validating an app’s functionality, usability, and security across different devices, operating systems, and network environments. However, choosing the proper testing framework can be daunting due to the sheer number of options available and the unique considerations of each one.

In the span of only a few months, AI has reshaped the landscape of almost every industry around the world in both positive and negative ways. Indeed, there is still a lot of room for improvement for this groundbreaking technology, but if businesses don’t embrace it, they’re sure to be left behind. In the QA industry, “AI testing” will become the norm in the next few years, bringing incredible advancements in the way we think and do software testing.

A deep learning framework is essential for building and deploying deep neural networks, a class of artificial intelligence algorithms inspired by the human brain’s structure and functioning. These frameworks provide a high-level interface, libraries and tools that simplify the development process of complex machine-learning models. Choosing the proper deep learning framework is crucial as it directly impacts your machine learning projects’ efficiency, productivity, and success.

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