API (application Programming interface) testing plays a crucial role in software testing. On one hand, it allows validation of the correct business logic and ensures that backend—or backstage—systems meet the specifications and the intended user experience.

For a time, desktop applications were declared dead. They became too costly to develop and deploy compared to web apps, which allowed businesses to easily onboard new users, release updates instantly and introduce recurring pricing models rather than one-off purchases.

Software teams today face intense pressure to deliver updates rapidly without compromising quality.

Have you ever thought of finding a bug in your software before it becomes a problem that costs too much to fix? That is what Shift Left Testing means—testing is done at the beginning of the development process. In the traditional approach, testing happens at the end of the development phase; defects found reduce development efficiency and lead to delays, high costs, and the risk of frustrated users.

Unit testing is a software testing method in which individual components or units of code are tested in isolation to confirm they function as intended.

In the evolving landscape of software testing, Behavior-Driven Development (BDD) has emerged as a powerful methodology for improving collaboration between technical and non-technical team members. However, the implementation of BDD tools like Gherkin and Cucumber often falls short of their intended purpose. Let's explore how to effectively use these tools and avoid common pitfalls.

Push notifications play an important role in user engagement and retention in your mobile app, keeping users up to date and creating a sense of urgency that leads to purchases. And in Android, we get the added benefit of Firebase Cloud Messaging (FCM) notification service, which acts as a middleman between the app server and the user’s Android device. It helps the delivery of push notifications, even if the app is not active or the user is using a different app on their device.

Understanding how your APIs handle and process data drives better decision-making. Different kinds of data flow through APIs and the services that consume them. But the actual information lives inside the HTTP request and response bodies. If you can effectively leverage that data with the contextual API metrics like status codes and routes, you can better understand how your customers consume your services.

The rise of AI has transformed how businesses operate, creating a surge in demand for AI-driven APIs, particularly those that leverage Large Language Models (LLMs). These APIs are at the heart of many modern applications, driving automation, customer interaction, and sophisticated data analysis. However, with this increased use comes a need for organizations to effectively monitor and manage the costs and consumption of these APIs.

Kafka is a highly popular realtime data streaming platform, renowned for handling massive volumes of data with minimal latency. Typical use cases include handling user activity tracking, log aggregation and IoT telemetry. Kafka’s architecture, based on distributed partitions, allows it to scale horizontally across multiple brokers. But although Kafka excels at high data throughput, scaling it to manage thousands of client connections can be costly and complex.