According to UBS, ChatGPT was the fastest growing consumer application in history. In just two months it went from launch to 100 million monthly active users. That led to a cultural and technological impact that saw Microsoft invest billions in the product’s parent company. But even if your product doesn’t break records, knowing the number of active users can help you understand its product/market fit, the impact of the changes you make, and how well the product retains user loyalty.

Processes are the foundation of your organization. They lay the groundwork for how your business operates, from product development and customer service to procurement and financial operations—and everything in between. Even work that isn’t formally defined in a process is likely a part of one. If you’ve ever tried to map or document a business process, you’ve probably learned that there’s no straightforward way to get it done.

In software performance testing the landscape is continuously evolving, driven by advancements in technology and the growing complexity of modern applications.

In the first three articles in this four-post series, my colleague Jason English and I explored DataOps observability, the connection between DevOps and DataOps, and data-centric FinOps best practices. In this concluding article in the series, I’ll explore DataOps resiliency – not simply how to prevent data-related problems, but also how to recover from them quickly, ideally without impacting the business and its customers.

Developer productivity is a complex subject for which there is no magic bullet. However, economic pressure, increased market competition and shorter delivery circles force many organisations to improve their efficiency and to open up new models of operations. Measuring, maintaining and eventually improving engineering productivity in an increasingly hybrid workplace are important discussions many organisations are having right now.

The rapid growth in software delivery as a solution for all business needs for organizations has intensified the demand for efficient and comprehensive software testing processes. Ensuring the quality, reliability, and security of software products is of paramount importance as they become increasingly integrated into various aspects of our lives.

This years I/O can be easily summed up by just one phrase: “AI”. AI absolutely everywhere. Sundar Pichai started, mentioned numerous times and ended the Keynote by mentioning AI. Some of the main announcements around AI for developers included the general availablity of “Bard” supporting 20 programming languages and use of the “PaLM 2” based models with some like “Gecko” small enough to run on mobile devices.

We’re thrilled to announce the latest release of Kong Insomnia, version 2023.2.0, packed with improvements and fixes that will make it even easier to manage your APIs. In this release, we introduce an enhanced Import flow, along with support for Kong 3.0 and the return of Swagger/OpenAPI preview. Continue reading to learn more about these exciting updates.

You know about different types of testing that follow a hierarchy: unit testing, integration testing, regression testing, acceptance testing, and more. But do you know all these testing types can be executed using different approaches? For instance, a popular approach to Unit testing is TDD (test-driven development). TDD involves creating the unit test code before building the application. Similarly, Incremental testing is a common and most used approach for Integration testing.

Front-end testing is all about ensuring everything looks and works as it should for the user. It involves testing the front-end code’s visual elements, user interactions, and responsiveness. There are various front end testing types- unit, integration, and end-to-end testing. Each type serves a specific purpose in ensuring the quality and reliability of the front-end code.