Systems | Development | Analytics | API | Testing

Across industries, AI systems are being scrutinized under new laws that demand proof of fairness, transparency, and human oversight. Hemraj Bedassee , Delivery Excellence Practitioner,

DevOps is a practice, not a tool, but tools are needed to implement it. Breaking down walls of communication and creating visibility and trust across all the teams involved in delivering software and technology is challenging. The right tools make the automation and integrations needed across functional teams seamless, open, and scalable. This article looks at the top CI/CD, automation, orchestration, and other DevOps pipeline tools to give you a detailed list of our Top 15 DevOps tools in 2025.

If you recall part one of this blog post, we were going to use ChatGPT in parallel with how we would work to cover these aspects of performance testing. We left the first part of this blog post at the point at which we had compared Requirements Gathering and Risk Assessment, we will pick this post up by looking at Script Creation before concluding with Results Analysis. Our performance testing tool of choice will be JMeter.

If you believe many articles online you would believe that automation in testing will soon be defined, managed and executed by Artificial Intelligence (AI). AI is embedded in many organisations technology landscape and to think that this model will change is shortsighted. AI is here to stay undoubtedly in one form or another, but should it be responsible for the automated testing of your applications under test?

When developers try to build realistic mocks and automated tests from production network traffic, the real challenge isn’t just in the capturing—it’s in the data manipulation. Raw traffic is a chaotic sea of patterns, dynamic tokens, environment-specific secrets, and tangled dependencies that seem impossible to untangle by hand. Over my two decades of building these sytems, I learned that solving this problem requires more than brute-force parsing or ad hoc scripts.

Census makes it easy to turn warehouse data into AI-ready vectors — without building a single pipeline.

With the release of Node.js 24.11.0 “Krypton”, the Node.js 24 line has officially entered Long-Term Support (LTS) and will continue receiving maintenance and security updates through April 2028. This marks the beginning of a new stable era for production workloads, bringing developers enhanced security, stricter runtime behavior, and improved Web API support.

Test automation has evolved far beyond QA. Today, it plays a direct role in product speed, developer efficiency, and even customer retention. That means one thing: it’s no longer just a technical investment. It’s a financial decision. If you’re a CFO, you’ve likely seen test automation mentioned in strategy decks or budget line items. But what does the return really look like?

What happens when an inquisitive, unpredictable user, without manual or training, just begins clicking and typing in your application? Will everything handle the unpredictability gracefully or crash prematurely? This chaotic scene is not hypothetical in the field of Quality Assurance (QA); it is actually an established testing technique called Monkey Testing. While structured testing is important, it often ignores the unstructured actions of actual users.