Systems | Development | Analytics | API | Testing

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?

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.

Knowing how well your application is performing is just as important as ensuring it doesn’t crash. But what defines “good performance?” How do you know if your app is hitting the mark or falling behind? With SmartBear Insight Hub, you can now use performance targets to set and track specific benchmarks for your application’s performance metrics.

Efficiency is critical in achieving successful releases. Manual updates to test plans, unclear test coverage, and poor traceability lead to delays, missed defects, and wasted resources.

At Tricentis, we’re committed to making test management smarter, more efficient, and customizable. With the latest release of Tricentis Test Management (TTM) for Jira, we’re introducing new features to enhance your test execution efficiency and provide greater control and visibility. Key updates include unified global and requirement execution progress reporting, greater AI customization across projects, and enhanced test review and execution.