According to the Chief Economists Outlook published by the World Economic Forum in September 2022, 7 out of 10 economists now consider a global recession at least somewhat likely. The sales organization has a front seat view to the impacts of recession as sales personnel hear their customers cite budget cuts, postpone purchases, and possibly look for less expensive alternatives.

So you’re already convinced that automated testing can transform the software development lifecycle and help produce higher-quality products. But convincing management it’s a worthwhile investment? Especially if they're hesitant to add technology they don’t understand to a tight budget? That's a different story. We've got you. Here are 10 rock-solid reasons why your organization needs a budget for automated testing.

For the next interview in our series speaking to AI leaders from around the world, we’ve welcomed experienced AI Specialist Speaker, Monish Gandhi of Gradient Ascent.

What’s the difference between knowledge and wisdom? Knowledge is knowing Excel is Turing-complete. Wisdom is not using Excel as your database. The best data engineering tools make your life easier. Speed up processes, simplify complex operations, give you insights into the machinery, and maybe save some $ along the way. In this article, we’ll give you an overview of the 7 best data tools for data engineering use cases.

Over the course of my career in testing and quality, I have seen many different quality strategies. All of them have been narrowly focused on testing, with perhaps a sprinkle of DevOps tossed in. I started to wonder why this is, and when I dove a bit deeper I realized that all of them were written by testers. It’s a truism in the industry that you can’t test quality into a product. Why is it then that all of our quality strategies revolve around testing or development methodologies?

As an API provider, once you’ve decided to bring in revenue from your APIs, the next step is to figure out how you will price the usage. As with any business decision, there are plenty of ways to go about pricing an API. There are many short-term strategies to establish initial pricing and then iterate to find which pricing model and price points work best for your customers.

Data warehousing requires data centralization, whereas data mesh enables a decentralized approach to data access. Organizations might think that the solution to their data management strategy requires a choice between the two, but the reality is that both approaches can and should co-exist.

When a project is sent to the testing team, the work starts for inspection of its quality before shipping it to the end user. As a responsible team, testers always want the number of bugs to be the bare minimum, with no complaints during the release. To bring this vision into reality, the testers have two paths they can take. The first path is aggressive.

Accentuating both quality and security while developing applications is the need of the hour. It helps ensure your application is free from all possible flaws and security loopholes that might lead to significant revenue losses. Testers all across the globe rely on both black box and white box testing to identify and remove all the critical errors before releasing the final product. In this article, I’ll go into detail about Black Box vs.

Bug tracking is one of the most critical stages in the software development lifecycle. It is crucial for software organizations to be able to capture, attract, and resolve bugs throughout the entire development process to release bug-free software. With software quality being the top priority for IT organizations, using defect tracking tools has become the new norm. So in this article, we will discuss the 9 best defect management tools in detail and how they stack up against each other.