Systems | Development | Analytics | API | Testing

Test case prioritization helps you rank test cases based on importance. It ensures you test the most critical features first. This approach saves time, improves software quality, and helps you catch high-risk defects early. Putu Kusumawardhani , Director, Client Impact,

Three years ago, I discovered Playwright Trace Viewer while helping our team debug a complex audio processing application. What started as curiosity became a game-changer that now saves us hours every week. We had this complex flow where users could upload audio files, apply effects, and export them. Screenshots were our go-to debugging tool, until they weren't. One day, a test kept failing in CI. The screenshot showed everything looked perfect.

AI-powered development is moving faster than ever. Tools like Cursor, Windsurf, and GitHub Copilot, and models like ChatGPT-4, Claude, and Gemini transform how software is built. These tools can generate code, solve problems, and deliver full applications in a single day, far quicker than traditional methods. But with this rapid development comes a major challenge: testing. While code is generated in hours, traditional testing tools and methods lag.

When you’re gearing up for a big launch—be it a product update, marketing campaign, or just steady growth—you want to know how many people can hammer on your app or website at once before things start to break. That’s where calculating concurrent users comes in. Whether you’re a non‑technical business owner, a student learning the ropes, or a seasoned DevOps engineer, this guide will walk you from the basics to advanced tips in a friendly, jargon‑free way.

Efficient management and updating of configuration files is essential in modern software settings, particularly when these files are stored on cloud storage services such as AWS S3. There are a number of issues associated with this process, such as maintaining data integrity, reducing downtime, and maximizing network and storage use.

Software quality today is not the responsibility of just one team-it’s a shared mission across development, QA, product, and operations. As systems grow more complex and software delivery cycles shrink, test automation has moved from being a luxury to an operational necessity. But adopting automation tools isn’t enough.

Back in our college days, we studied about the Software Development Life Cycle (SDLC), right? In that, we learned about various software methodologies like the Waterfall model, Agile, etc. We also learned how these methodologies help us deliver software quickly. Rapid Application Development is one such methodology we forgot to study in college – no worries, now you can understand what exactly Rapid Application Development is and how it helps you and your business.

Let’s get straight to the point—this post is vital and couldn’t have come at a better time. As QA professionals, we’ve always been the gatekeepers of software quality. But with the rise of AI and LLMs, our role is evolving. Writing evaluations—assessments of AI systems—is quickly becoming a core skill for anyone working with AI products, and soon, this will include nearly everyone in tech.

With Deloitte predicting that 25% of companies using Generative AI will launch agentic AI pilots or proofs of concept in 2025, is your testing strategy ready for the agentic revolution? This highlights the pace at which the modern software development industry, already demanding continuous operational speed improvements, heightened efficiency, and superior product quality, is turning to advanced AI.