Software applications often perform well during short-term testing but encounter issues during continuous use.

When was the last time you used a chat service and noticed messages arriving out of order? It’s likely that you can’t think of one. Getting messages to arrive in the right order is more or less job number one of a chat service. Sounds simple. But scale this to millions of users across a global network and it becomes a complex distributed computing problem. Even at smaller scales, message ordering isn’t guaranteed once you go beyond a simple, direct connection between two users.

It’s November 2024 and I think it is safe to say, AI is officially everywhere. People are talking about it in cafés worldwide. So are your kids. Or your parents. Or grandparents. It may have been integral to your last car buying experience and has been used as a recommendation engine by your at-home/connected devices that hear you and want to improve your life by recommending products they think you need. It is pervasive and getting more so every day.

Enterprise organizations collect massive volumes of unstructured data, such as images, handwritten text, documents, and more. They also still capture much of this data through manual processes. The way to leverage this for business insight is to digitize that data. One of the biggest challenges with digitizing the output of these manual processes is transforming this unstructured data into something that can actually deliver actionable insights.

Today, cyber defenders face an unprecedented set of challenges as they work to secure and protect their organizations. In fact, according to the Identity Theft Resource Center (ITRC) Annual Data Breach Report, there were 2,365 cyber attacks in 2023 with more than 300 million victims, and a 72% increase in data breaches since 2021. The constant barrage of increasingly sophisticated cyberattacks has left many professionals feeling overwhelmed and burned out.

In modern software development, clear code documentation and commenting are not only just a part of good practices, but also they are essential for effective collaboration, maintainability, and scaling projects over time. However, they are often neglected due to time constraints or lack of clarity on how much detail is necessary. But, with the advent of artificial intelligence, developers now have tools to streamline this process, allowing them to focus on coding without compromising on clarity.

Over the years, CI/CD has become such an integral part of DevOps that it is difficult to think of one exclusive of the other. However, DevOps’ much broader set of utilities might often undermine the role and scope of CI/CD. The vast DevOps toolchain for configuration management, IaC, monitoring, security, and other such use cases confines the CI/CD strategies to mere code handling.

A web development company needs to determine the right tools and frameworks required for streamlining processes and testing efficiency, hence proper consideration is needed. The global automation testing market size was estimated at USD 15.39 billion in 2023 and is projected to reach USD 17.71 billion in 2024. Notably, in 2023, North America dominated the global market with a leading share of 24.37%.

In recent years, the serverless paradigm has become known as a transformative approach to application development, allowing us to focus more on writing code and less on managing infrastructure. Laravel Vapor, a serverless deployment platform for Laravel applications, brings the power of the Laravel framework into the world of serverless architecture.

Diego Molina takes you step-by-step through how to get started with automated testing.