A quality assurance (QA) test plan or a software test plan is a document that outlines the steps, approaches, tools, and best practices for carrying out QA testing for your project. The purpose of a QA test plan is to define testing goals and objectives while considering cost, project requirements, and delivery timelines. Ultimately, this document ensures that all stakeholders are unified in their commitment to delivering a high-quality product.

Smoke testing and regression testing play crucial roles in ensuring the functionality and reliability of software applications. Although both test software’s functionality, their purposes differ, and they occur at different stages of development. Understanding the differences between these two testing types can help effectively manage and maintain software quality.

Does your application function as expected across the many devices your customers use? People use the same application across a wide range of gadgets, including mobile phones, smart TVs and streaming devices, gaming consoles, and desktop web apps. It’s imperative that all versions of your application work consistently and as expected across various platforms.

Your organization is likely sitting atop vast quantities of diverse data. However, all that data must undergo certain processes before you can fully leverage it. Data discovery is among those processes. Let’s learn more about it.

Workflows determine how organizations conduct processes. These workflows can initially be very straightforward—just a simple series of steps that must be completed sequentially to achieve a particular outcome. However, the steps can become more complicated as business processes and requirements become more extensive. Your organization needs to keep these workflows running smoothly even as they become more complicated.

Smooth and effective data migration helps organizations move data across systems efficiently to maintain their competitive advantage. Still, Gartner reports that only 17% of initiatives involving data migration are completed within their budgets or set timelines. Understanding these data migration challenges is the first step toward overcoming them. In this blog, we’ll explore data migration and its different types, challenges, and strategies for dealing with them.

Organizations today are accumulating data more than ever. Traditional data management approaches, such as centralized data warehouses and siloed data marts, are struggling to keep pace with the ever-increasing volume, velocity, and variety of information. The complexity of modern data environments is outpacing the capabilities of these legacy systems and demands a more agile and distributed solution.

Imagine launching your software with confidence, knowing that every button click, data transfer, and user interaction works perfectly—no surprises, no last-minute bugs. This level of assurance doesn't happen by chance; it’s the result of accurate End-to-End (E2E) Testing. But what exactly does end-to-end testing mean? How does it ensure that your application not only works but grows under real-world conditions?

See a solution that bridges the gap between gRPC services and RESTful HTTP APIs, and is enhanced with Apigee’s enterprise API management platform.

Ready or not, AI workflow automation is poised to transform business operations from the shop floor to the C-suite in the AI economy. As organizations embrace digital-first initiatives, IT teams will be able to do much more with less. The situation is a byproduct of the generative AI boom. And yet, so many companies have hardly scratched the surface of AI automation’s full potential in their business operations.