Systems | Development | Analytics | API | Testing

At times, test automation can be a bit of a pain. You spend all this time writing tests, only to have them break the moment someone tweaks a button. Your test suite is now full of red, and you’re stuck debugging instead of shipping features. It’s frustrating and, frankly, it slows everything down.

Automated functional testing is more than just running tests on autopilot. It's a way to ensure that your software behaves as expected, across all features and platforms, without slowing down development. But it’s not automatic by default. To get the most out of your efforts, you need to apply the right strategies from the start. That’s where automated functional testing best practices come in. They help you avoid brittle scripts. They reduce maintenance headaches.

Regression testing helps you make sure that old features still work after new changes are made. With automation, this process becomes faster, more reliable, and easier to scale. But automation can easily become messy. Tests break. Suites grow too large. Bugs slip through. That’s why you need a strategy: one that focuses on the right automated regression testing best practices.

Software updates are inevitable. New features get added. Old bugs get patched. But with every change, there’s one big question: what might break? That’s where a solid regression test strategy comes in. A regression test strategy gives you a reliable process to make sure your existing features still work after each update. Without it, even the smallest change can lead to unexpected bugs in places no one thought to look.

Distributed tracing is a method to track requests across interconnected systems, providing visibility into how data flows through complex pipelines. It helps identify bottlenecks, troubleshoot errors, and improve system performance. Here's what you need to know: Why It Matters: Traditional logging often misses the big picture in distributed systems. Tracing connects the dots, enabling root cause analysis, performance monitoring, and improved reliability.

As organizations increasingly rely on AI to power their products and services, addressing bias is now a critical responsibility for quality and engineering leaders.

We're excited to announce an enhancement to the WSO2 Support Portal: our new Call Management feature! This update is designed to streamline how you request and manage calls for your support cases, making the process more efficient and transparent.

The API ecosystem is rapidly expanding into the world of AI. Enterprises are increasingly integrating generative AI services like OpenAI, Claude, and AWS Bedrock into their workflows, but face challenges with secure, governed, and scalable integrations. That’s why Bijira, WSO2’s AI-native API management SaaS platform, introduces AI Gateway support. This is a purpose-built solution to create, expose, and manage AI service integrations as first-class APIs.

By Noam Harel, Co-founder and CMO, ClearML AI is certainly transforming industries, but delivering it at scale is a harder task The shift to enterprise-grade AI isn’t just about building better models. It’s about managing the growing sprawl of infrastructure, tools, and people involved in every phase of your AI production From building and training to production deployment, teams are bogged down by fragmented workflows, manual provisioning, inconsistent environments, and underutilized compute.