Your test management platform allows you to create test cases and assign them to testers. It makes it easy to analyze your results. But it doesn’t help you manage all of your testing resources in one place, and that’s a huge problem. In today’s world of online, remote work and the rise of the gig economy, distributed testing is not only widespread—but it also provides countless benefits to product and engineering teams (including faster releases).

Unless someone at your organization has built internal QA software, your in-house testers and your external testing resources are not testing under the same platform. We know, because no collective testing software exists, until now that is. We’ve enabled groundbreaking collective testing capabilities under our test management platform to keep your testing under one roof. But what does this really mean? And why should you—as a QA manager, dev team leader, or DevOps manager—care?

The success and growth of companies can be determined by the technologies they rely on in their tech stack. To deploy AI enabled applications to production, companies have discovered that they’ll need an army of developers, data engineers, DevOps practitioners and data scientists to manage Kubeflow — but do they really? Much of the complexity involved in delivering data intensive products to production comes from the workflow between different organizational and technology silos.

Well, it is International Fraud Awareness Week, focused on promoting fraud prevention and education. A fantastic initiative! Maybe I am naïve but I feel a bit sad that there is a need for “fraud week”. The insurance industry has a long and intimate relationship with fraud in many different ways. Insurance fraud can take place at a process or business function level, most notably in claims or underwriting.

As a specialized and mature form of embedded analytics, contextual analytics is a game-changer if you're a software vendor looking to further augment your customers’ user experience, without requiring developers to completely reengineer your offering. Contextual analytics blends the data your users need for decision-making right at the point of their daily work, directly inside the interface and transaction flow of your software.

In my last posts, we talked about how to contribute to Selenium, and why we’re bumping the version number to 4. That’s enough preamble! Today, we’ll cover some details about the new tricks and capabilities that Selenium 4 offers.

Regression Testing, as all Quality Assurance professionals know, is ensuring that previously developed and tested software continues to operate after a change. Performance Regression being a subset of regression testing as a discipline is therefore ensuring that previously developed and tested continues to meet its performance criteria after a change.

Even before COVID-19 became the biggest challenge to operational resilience in a generation, organizations were struggling with a gap between digital transformation and internal processes that felt decidedly “pre-digital.” Automation presents a solution, connecting new tools to old, legacy systems through three key shifts in technology.

F5 Networks modernized its data stack, boosted time to insight, and placed actionable data in the hands of the right decision-makers. F5 Networks is a Seattle-based application services and application delivery networking company. Because its revenue depends on speed and accuracy, the company is always looking for ways to improve business insights and support data-driven decision-making.

Data analysis is incredibly useful for all kinds of businesses and also has academic and hobbyist applications. Nonetheless, it’s still possible to fall into numerous traps when trying to accurately interpret your data. That’s why we’re giving you a list of the top 8 common data analysis mistakes to avoid at all costs. Our first expert Jitin Narang, CMO at TechAHead contributed the following five top data mistakes to avoid: