Systems | Development | Analytics | API | Testing

For today’s IT Director, the pressure has never been greater. Tasked with delivering business-critical technology in a landscape of shrinking budgets and expanding expectations, IT leaders find themselves at the intersection of business demands and technical realities. On one side, C-suite executives and department heads expect solutions that improve efficiency, drive innovation, and deliver competitive advantage.

As federal agencies adapt to tighter budgets and smaller teams, they must find smarter ways to deliver secure, high-performing services. Automated testing offers a practical path to streamlining processes while safeguarding performance, security, and reliability. Government software development is undeniably complicated. Agency teams balance compliance, security, and legislative requirements while supporting legacy systems, mobile apps, and the latest technologies.

APIs (application programming interfaces) have transformed how we build modern software systems. As our digital experiences become more modular and interconnected, APIs power mobile apps, partner integrations, and internal services. Most companies now expose APIs not just for technical access, but as part of a larger API business model. But simply deploying APIs through a gateway like Gravitee doesn’t suffice.

Software development has always evolved with new paradigms to meet the growing demands of modern systems. One of the most significant shifts has been the adoption of microservices. Emerging in the early 2010s, this architectural pattern moved away from monolithic applications in favor of smaller, independent services that interact with each other over a network.

Recent leaks have once again brought API and data security concerns into the spotlight. Specifically, the leaks pointed at the following as major areas to consider when thinking through your API and data security strategies: When considering the above, it’s critical to think about best practices across every stage of the API lifecycle, starting with the initial phases of API design and testing.

Kong is ranked number 1 on the G2 Momentum Grids for API Management and API Development for Spring 2025. And users seem to agree with the glowing assessment: Kong is currently the highest-rated tool based on user satisfaction in the categories of API Management and API Development on G2. We think there are plenty of reasons why Kong is king when it comes to API management and API development. But don't just take our word for it.

Quality can no longer be an afterthought and traditional testing approaches often struggle to keep up with modern applications’ complexities. This is where data-driven testing comes in - bringing predictive insights to the forefront of QA. Data-driven testing enables a strategic approach to quality, where every test is backed by insights. As the world moves into a future driven by AI, automation, and continuous testing, embracing data-driven testing became essential.

Have you ever wished you could just ask your CI platform a question instead of digging through logs or scrolling through builds? Well your wish has been granted: Bitrise MCP (Model Context Protocol) Server is here, a new, conversational way to interact with your CI.

API’s have become the backbone of modern digital ecosystems, enabling seamless integration and interaction between diverse systems, applications, and services. Organizations increasingly rely on APIs to deliver robust functionality, enhance user experiences, and drive operational efficiency. Additionally, APIs are playing an even greater role in the future of AI-driven systems.

Using AppSignal, you can monitor the performance of your Redis calls and get insights into how your application uses Redis. The AppSignal Python agent supports instrumenting Redis calls out of the box, so you can start monitoring your usage with just a few lines of code. In this post, we will cover how to instrument Redis in a Python application using AppSignal. We will build a simple URL shortener that stores data in a local Redis instance and uses AppSignal to monitor performance.