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

Software Deployment In 2026: Checklist & Strategies That Work

Software deployment looks simple on paper, but in real projects, it’s where most failures show up. Even stable code can break when deployment isn’t planned well. In 2026, software deployment is no longer just about pushing code – it’s about reliability, speed, and control. Let’s explore how modern teams can deploy smarter, faster, and safer in 2026.

Supercharge your LLM Using Production Data Context

Are your LLM coding agents (like Cursor or Claude Code) hallucinating fixes because they don't know what's actually happening in production? In this video, Matt from Speedscale shows you how to bridge the gap between your local IDE and live production traffic using the Model Context Protocol (MCP). Most observability tools just give you telemetry. Speedscale’s MCP server gives your agent the "inner workings" of actual API calls and payloads, so it can check its assumptions against reality. No more "vibe-coding" and hoping it works; let your agent find the 500 errors and rate limits for you.

How do you plan to test 10x more code with the same old tools?

You can’t test 10x more code with the same old tools. As AI dramatically increases code volume and speed, traditional testing becomes a bottleneck. Teams need AI embedded across the entire testing lifecycle to scale testing, boost productivity, and keep releases moving fast without sacrificing quality — Alex Martins, VP of Strategy at Katalon Follow Katalon for more insights in our series!

A Shifting Left Success Story | David Ingraham | TTTribeCast Webinar

A Shifting Left Success Story” takes you inside a real-world transformation where test automation was intentionally moved earlier in the development lifecycle — with measurable and lasting impact. This session unpacks the how, why, and key lessons learned from embedding Shift Left practices within a cross-functional team. You’ll discover what made the approach successful, where challenges emerged, and how a thoughtful Shift Left strategy can dramatically improve code quality, shorten feedback loops, and build greater trust between developers, testers, and product stakeholders.

Agentic AI: From Reactive Bots to Autonomous Digital CoWorkers | Toni Ramchandani

Agentic AI marks the next evolutionary leap in artificial intelligence - systems that don’t just answer prompts or generate content, but plan, decide, and act on our behalf with minimal oversight. In this webinar, we’ll demystify what “agentic” really means, trace the shift from single‑step chatbots to multi‑step autonomous agents, and explore the architectures—sense‑plan‑act loops, large‑language‑model reasoning layers, and tool‑integrations—that make true agency possible.

AI in Action: Powering the Future of Testing | Xray Webinar

A quick overview of Xray Test Management - cutting-edge test management app for Jira. Xray is the leading Quality Assurance and Test Management app for Jira. More than 4.5 million testers, developers and QA managers trust Xray to manage 100+ million test cases each month. Xray is a mission-critical tool at over 5,000 companies in 70 countries, including 137 of the Global 500 like BMW, Samsung and Airbus.

Let Your LLM Debug Using Production Recordings

Modern LLM coding agents are great at reading code, but they still make assumptions. When something breaks in production, those assumptions can slow you down—especially when the real issue lives in live traffic, API responses, or database behavior. In this post, I’ll walk through how to connect an MCP server to your LLM coding assistant so it can pull real production data on demand, validate its assumptions, and help you debug faster.