Systems | Development | Analytics | API | Testing

Android test automation is the process of using software tools and scripts to execute tests on Android applications automatically.

Kong customers include some of the most forward-thinking, tech-savvy organizations in the world. And while we’re proud to help them innovate through traditional APIs, the reality is that their ambitions don’t stop there. Increasingly, our customers are investing heavily in real-time data and event streaming.

During our recent webinar Quality Horizon 2025, the virtual room buzzed with energy, filled with insightful questions that pushed our thinking forward. But one particular query truly struck a chord, a question that elegantly highlighted a core challenge in AI-driven testing: The observation was spot on. It perfectly captured a critical limitation we’ve seen across the current AI testing landscape.

Front-end testing ensures your application looks and behaves as users expect on every device, browser, and interaction. Whether it's clicking a button, filling a form, or navigating pages, front-end tests validate what users actually experience. But manual front-end testing slows teams down. As the interface evolves, so do the tests, and without automation, keeping up is nearly impossible.

Test automation is essential if you want to move fast without breaking things. But here’s the hard truth: not every test is worth automating. And trying to automate everything is how teams burn time, introduce flakiness, and end up maintaining tests that add zero value. So how do you know what test cases to automate? That’s what this guide is for.

AI is transforming software testing by introducing intelligent automation techniques. Unlike traditional scripts that follow static instructions, AI-driven testing uses machine learning, computer vision, and NLP to adapt and make data-driven decisions during testing. This shift offers significant advantages. AI can rapidly analyze large datasets (requirements, code changes, past failures) to identify high-risk areas and prioritize testing efforts.

ClearML v3.25 introduces native support for vector databases within the Hyper-Datasets feature. This release enables users to store and search embeddings directly inside ClearML, opening the door to powerful custom RAG pipelines. In addition, v3.25 includes expanded orchestration metrics, new Application Gateway UI, and a range of UI upgrades to streamline day-to-day operations.

BigQuery ML contribution analysis models, now GA, automate insight generation and output key change drivers from multidimensional data.

Choosing the right relational database management system (RDBMS) is a critical decision for any organization. Two of the most widely adopted options are Microsoft SQL Server and MySQL. Both platforms have evolved rapidly, introducing new features and enhancements that reflect the changing needs of modern data-driven applications. This updated 2025 comparison explores their latest capabilities, performance, security, and suitability for different use cases.

In today’s API-driven world, strong API design is key to building reliable and scalable services. Without structure or standardization, the design phase can quickly spiral into chaos. Common problems like misaligned teams, tightly coupled systems, code duplication, and inconsistent standards begin to snowball. This causes delays, poor customer experiences, and in some cases, long-term technical debt that becomes a serious business risk.