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The latest News and Information on Software Testing and related technologies.

How to make APIs AI-ready | Automating reviews with Swagger Studio & Spectral

As AI agents increasingly interact with APIs, design clarity and structured metadata matter more than ever. In this focused demo, Senior Solutions Engineer Mairtín Conneely take us through how to use Spectral rulesets in Swagger Studio to automatically enforce AI-ready API design standards across your OpenAPI definitions. This video covers:What “AI-ready” API design meansCreating custom Spectral rulesImporting governance rules into Swagger StudioRunning automated AI-readiness checksScaling API quality with governance automation.

Did you expect the implementation to take longer than it actually did?

Yes, the team expected the implementation to take longer. Given the client’s secure environment, multiple pre-production setups, and many agile teams, the plan allowed for nine weeks or more. In reality, the implementation was completed in about six weeks, even with an unplanned pause, which exceeded expectations and highlighted how smoothly the rollout went — Mush Honda, Chief Quality Architect at Katalon.

Scalable Version Control For Professionals: Meet the Perforce P4 Platform

Discover how Perforce P4 has evolved into a complete version control platform powering everything from AAA games to blockbuster VFX to chip design. Teams use it for more than version control—they get workflow control that empowers creative freedom, no matter how massive the project or tight the deadline. You'll see how the P4 platform connects your entire workflow.

Debugging Encrypted Microservice Traffic with Speedscale's eBPF Collector

Production bugs that only reproduce in actual traffic can be some of the most frustrating bugs in software development. You can stare at your logs, add traces to your code, add instrumentation – and still not be able to see the actual requests that went over the wire. And that gets even harder when the requests are encrypted and the system is a black box. You can use tools like Wireshark or Kubeshark to capture the requests.

Spring Boot API Testing: A Practical Guide for Enterprise Teams

Enterprise Spring Boot APIs should be tested at three levels: unit tests for business logic, integration tests for external service behavior, and traffic replay for production edge cases. Most teams only do the first. This guide shows all three using a real Spring Boot application that calls external APIs (SpaceX, US Treasury) with JWT authentication. The kind of service that looks simple in development and breaks in production.

Beyond Left and Right: Why "Shift Everywhere" is the Future of DevOps

Modern software architectures have rendered traditional QA obsolete. In an era of distributed microservices and serverless functions, bugs are no longer just code errors; they are systemic interaction failures. While Agile successfully accelerated delivery, it left a critical gap in quality assurance. The industry's initial response, splitting focus between "Shift Left" and "Shift Right", created a fragmented safety net.

7 things engineering teams get wrong about AI-powered QA

We’ve all been there. When engineering teams evaluate AI-powered QA tools, the same questions come up again and again. Some are rooted in genuine technical curiosity. Others stem from experiences with earlier-generation tools that earned a healthy dose of skepticism. After hundreds of these conversations, I’ve identified the seven most common misconceptions. Contents Toggle.

Agentic Payments: Redefining the Future of Payments for Enterprises

‍ Enterprise payment systems are at a breaking point: rising volumes, tighter margins, and ever-more sophisticated fraud are pushing traditional automation to its limits. The AI-enabled payments market was valued at $38.36 billion in 2024 and is projected to grow over the next decade. As firms seek smarter, real-time decisioning and risk control, highlighting how indispensable AI has become in payment stacks today. -

Tricentis AI Workspace: The new control plane for autonomous quality engineering

AI is reshaping how software gets built, tested, and delivered. For quality engineering teams, AI agents promise extraordinary acceleration by automating analysis, executing tests, generating assets, and orchestrating tasks across the SDLC. But when enterprises begin experimenting at scale, new challenges appear. Where are these agents running? What exactly are they doing? Who approves their decisions? How do we govern them safely?