Systems | Development | Analytics | API | Testing

Imagine a customer who is about to buy some more things, but their cart is already full of products, so they go to checkout. Every process is done but when it comes to payment, it fails! After that, the customer got frustrated and left the cart. By this example, you may understand that every customer needs a smooth payment experience. A small glitch can cost businesses customers and revenue. Payment gateways connect buyers and sellers to make secure payments and transactions.

Ensure your apps are accessible for your users and to follow compliance. Cypress has emerged as a powerful tool to make sure web applications meet rigorous quality standards, learn more in this article.

Before diving in, it's important to define three key concepts: However, traditional methods often have limitations. Fixed scenarios can miss unpredictable behaviors and identifying specific bottlenecks can be challenging without real-time insights. This is where AI comes in. In the following sections, you’ll understand how Artificial Intelligence (AI) might enhance performance testing, from creating adaptive test scenarios to optimizing resources.

The pod model is an innovative approach that many enterprises, both small and large, are adopting to enhance their software development and quality assurance processes. This model involves creating small, cross-functional teams known as pods. Similar to an Agile or scrum team, each pod typically consists of five to ten members, including developers, testers, and a quality engineer (QE) or quality assurance engineer (QAE).

When selecting a test automation solution for your mobile applications, it quickly becomes clear there are many decisions to consider.

Digital transformation is no longer optional—it’s essential for staying competitive, driving value, and meeting evolving customer expectations. As Brian Solis puts it, “Digital transformation is to change the way you compete, how you create value, and how you can use digital tools and technology to be relevant as the world continues to evolve.” Organizations worldwide are taking this to heart, with spending on digital transformation expected to hit $3.9 trillion by 2027.

It’s not enough to just test our code's happy path (in other words, the error-free path we hope our users will take). To be really confident our code can’t be abused, either accidentally or on purpose, we must actively attack it to try and find ways of breaking it. If we don’t do this, someone else will, and they probably won’t be as friendly.

Imagine building a system where you want to validate if the logic implemented would fit in when the entire system is built, but for that, you would have to build the entire architecture (well, a lot of resources are spent). Now imagine a way where you can simulate the versions of real objects or components. This is where Data Mock comes into play!