Many organizations struggle with fragmented testing approaches covering only individual components or features, leading to gaps in the system’s reliability and user experience.

Over one billion people worldwide live with some form of disability. Accessibility testing ensures that digital platforms are usable for everyone, including people with disabilities.

Within the domain of software quality assurance, functionality and user experience hold a paramount position, and visual testing becomes an indispensable protector of the digital environment. The complexity of modern apps is always growing, thus it's critical to guarantee pixel-perfect precision and smooth visual consistency across all platforms and devices.

We at Loadero regularly plan, implement and execute performance tests for clients which has led to us acquiring plenty of first-hand experience on the topic. Within this article we want to share with readers exactly what performance testing is, why it is valuable, as well as cover several factors that should be taken into consideration when deciding on the tests' implementation.

So in today’s world of software development, where things happen so fast, test automation is no longer an option but a necessity. Thus, it helps developers shift their attention to creativity and develop new features rather than being bogged down by monotonous microtasks. But here’s the catch: automation isn’t an effortless solution. If not handled properly, it can create more issues than solutions—poor-quality test code, unreliable (flaky) tests, and holes in test coverage.

Complexity is increasing exponentially. Compared to just 20 years ago, our personal lives and the way organizations do business has changed completely. This translates into increasingly complex and interrelated business processes and a sprawl of IT systems that can only function when each of its components runs flawlessly. Navigating this business/IT complexity can only be overcome by combining business process solutions, quality assurance tools and human ingenuity and expertise.

Generative AI in manufacturing refers to using advanced AI algorithms and generative models to optimize various aspects of the production process. This technology enables manufacturers to create innovative product designs, streamline workflows, predict maintenance needs, and boost efficiency in frontline operations. By integrating generative AI, manufacturers can enhance decision-making, collaboration, and data insights, ultimately improving overall performance.

The dynamic landscape of product development continuously increases the scope of what is demanded from quality engineering (QE) professionals. So, it is not surprising that workplace learning and upskilling are critical to the organizational success, as highlighted by Workplace Learning Report: Without a doubt, the more technical side of the skill spectrum - e.g.

Every QA tester knows: time is money. When something breaks down on your website or web application, it can cause major issues for users within minutes. Because you’re constantly working to prevent fires from spreading (or happening at all), it’s important to have numerous strategies at the ready to help you manage your time wisely. One of these QA testing timesavers is called smoke testing. But exactly what is smoke testing?