Systems | Development | Analytics | API | Testing

Did you know the global digital health market is on track to surpass $660 billion by the end of 2025? That’s no surprise, considering how healthcare apps have become indispensable in our daily lives.

As of 2025, Android powers approximately 71.85% of the global mobile operating system market. For developers and QA teams, this variety is both exciting and overwhelming.

Advances in medical robotics and healthcare technology — enabled by the Internet of Medical Things (IoMT), Machine Learning (ML), and Artificial Intelligence (AI) — mean that we are already seeing less invasive procedures, more accurate diagnoses, and more personalized treatment options for patients. But these innovations depend on software which brings increased safety and security risks.

By using a continuous quality approach, retailers can build high-converting mobile apps that shoppers will love.

Traditional test automation usually needs a lot of coding skills, which makes it take heavy resources time-consuming, and unscalable. In an increasing rate of software development speed, QA teams have to adhere to the fast release cycle while still maintaining the quality. This is happening now, and by 2025, low code and no code test automation will be the center of QA. But why? What drives these companies to jump into such new, relatively unstable platforms?

Continuous Integration (CI) is the backbone of modern software development, enabling developers to detect and fix issues early by integrating code changes frequently. But what’s a superhero without their gadgets? That’s where CI testing tools swoop in! They ensure your builds are solid, your tests are green, and your sanity remains intact. In this blog, we’ll dive into the world of CI testing tools, unraveling their magic and exploring some of the most popular options available today.

Using a mock server is a popular method of working around these limitations and realities, allowing you to test web server assets against specific requests, ensuring that your response data matches the expected outcome. Today, we’re going to look at a powerful solution for Python clients in the form of MockServer. We’ll walk through the tool’s basics and learn how to use it for your own testing.

Software and quality assurance teams use AI in all parts of the automated testing workflow. According to a survey of 625 software developers we ran, 81% teams use AI tooling in their testing workflows for some variety of test planning, test management, test writing, and even analyzing test results. But AI can make the biggest impact on the most time-consuming steps in the automated testing process: test creation and maintenance.