Labs Blog

In the DevOps world, there is no single magical tool that fits all the needs. It is about choosing the right tool that fits an organization's needs. Let’s find out about them.

What technologies do you see contributing to this new reality of a modern work week, and how would you implement it as the manager of your teams?

Welcome to the FUTURE! It is the year 2020, the year of cyberpunk. The hoverboards of 2015 are now only a vague memory, something that "used to be" and Blade Runner happened in November of 2019. So clearly we are now in the utopian times of technology.

Take a look at our most read and shared blog posts from December 2019

As the new year starts, many of us have New Year's resolutions, and many of those will eventually perish within a month or two. I don't have any resolutions. The only thing I try to do each year is to learn a new technique or language.

Last week, Gartner published the always eagerly awaited Magic Quadrant for Software Test Automation. And over the last few days, it has been circulated and quoted a lot of times. But what does this all mean? Are the non-Leaders tools not worth it? Do we need to follow the leader(s)? Are there only 10 automation tool vendors?

AI is at the core of the digital transformation that is improving the economics and capabilities of all aspects of business, including customer service. AI can help complete repetitive, predictable tasks — or take over those tasks completely — and interact with customers autonomously to add value.

In this blog post, we will highlight and feature our most read and shared posts from November 2019.

Exploring the advantage of service virtualisation deployment in current IT trends.

DevOps is the Mount Olympus of the new software delivery civilization. And in the Mount Olympus of Dev(QA)Ops we have also the Twelve Olympians. Let's take a look.

Here is a quick roundup of initiatives from around the world where AI has been deployed for solving environmental and social challenges

Test data architecture has often been identical to production data architecture – perhaps on cheaper infrastructure. Why should the Test Data Architecture differ from the Production Data Architecture?