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

How Enterprise Teams Are Validating AI-Generated Code at Scale | Perforce 2026

Your testing strategy was built for a world before AI wrote code. That world is gone. AI is now generating code, reviewing pull requests, writing tests, and analyzing defects, faster than any team can validate it manually. In this session, Perforce CTO leaders Anjali Arora and Rod Cope sit down with VP of Product Steven Feloney to break down why traditional test automation can't keep pace, and what comes next.

NeoLoad 2026.2: Enable scaled performance validation across teams

Performance engineering teams are already stretched. AI-accelerated development means more code, more releases, and more pressure on the people responsible for making sure it all holds up. Tricentis NeoLoad 2026.2 is built around a straightforward premise: performance validation must scale to match the pace of delivery, and that means more than just making specialists faster.

How to design & test APIs with OpenAPI & Swagger | What's changed in 3.1 & 3.2

Outdated API docs and last-minute bugs cost teams time and trust. Learn how the OpenAPI Specification help you document, govern, and test your APIs from design to deployment – all inside SmartBear Swagger. SmartBear's Yousaf Nabi, Developer Advocate, and Chris Armstrong, Manager of Developer Relations, explain why API documentation drifts out of sync and walk through what's changed between OpenAPI 3.0, 3.1, and 3.2. After covering a brief history of Swagger and the OpenAPI specification, they demo the full API workflow across Swagger.

Agentic AI Test Execution Inside Jira with Xray and Lynqa

AI is becoming part of every stage of the testing lifecycle. Teams are using it to analyze requirements, design test cases, generate automation scripts, and accelerate execution activities that previously required significant manual effort. Within Xray, AI already helps transform Jira requirements into actionable test cases with AI Test Case Generation.

AI is Exposing Observability's Dirty Secret

The 3 pillars of observability are breaking. For years, dev teams relied on Logs, Metrics, and Traces to know when something went wrong. But now? AI agents are writing, deploying, and changing code in real-time. When an AI hallucination pushes a bug to production, standard monitoring sees nothing wrong.To survive the AI era, we need a 4th Pillar of Observability. Watch to find out what it is and why the old way of monitoring just became obsolete.

Disaster Recovery for Retail: Are You Protecting Systems or Protecting Revenue?

Every year, retailers invest millions in uptime. Yet when disaster strikes, most discover their DR plan protects infrastructure, not the business that runs on it. “It wasn’t the outage that hurt us most. It was the 45 minutes we had no idea it was happening.” – CTO, UK fashion retailer, post-incident review You have a DR plan. Your infrastructure team tested it six months ago. Your board presentation has a green tick next to Business Continuity.

How to Create Data-Driven Load Testing Scripts for Complex User Journeys (2026 Guide)

Picture this: you’ve completed a round of performance testing, and your load testing scripts all pass with flying colors. The release goes live, but within hours, users report sluggish performance and intermittent failures. What happened? In most cases, traditional scripts relied on static payloads and linear request patterns, failing to reflect the unpredictable, multi-step journeys of actual users.