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

In performance testing, AI's confidence can be your team's undoing

Quick summary: AI accelerates code creation, but its inherent confidence pushes structural risks downstream, where they surface as costly, release-blocking problems. As code output scales, performance validation that can’t keep pace becomes a headache and a business risk. Agentic performance testing embeds skepticism and performance awareness into the development process before risk can compound. Software development requires specialized expertise for a reason.

AI is writing your code. Is your regression testing keeping up?

AI is now writing more of your code than ever. But the problem is that your test suite was built to catch errors, not to catch the difference between what an AI agent produced and what your original specification actually required. As AI tools accelerate development velocity, the volume of code moving through pipelines is outpacing traditional quality processes.

Agentic Testing and QA: Why Chrome DevTools Still Matters for Modern Testers

Chrome DevTools is the built-in browser inspector and debugger that ships with Google Chrome, giving testers ground-truth visibility into DOM state, network traffic, device rendering, and runtime behavior. In the context of Agentic Testing and QA — the emerging pattern where AI agents draft, execute, and summarize tests with reduced human supervision — DevTools remains the verification layer that confirms what an agent actually did inside the browser.

Rethinking DevOps Testing: Why Unified Approaches Matter More than Ever?

Embedding software testing at the core of the DevOps lifecycle is imperative in today’s highly competitive software development world. Continuous integration, rapid deployments, and tight feedback loops have become standard practice. Yet many organizations still approach testing with outdated assumptions.

Complete guide to understanding vision AI for object recognition | TestComplete

Testing complex UI elements like CAD software, Google Maps, or Citrix environments often leads to brittle tests and false negatives. Vision AI solves these automated testing challenges by recognizing elements just like a human would, reducing manual testing efforts, and improving accuracy. Discover how vision AI strengthens automated testing for visually complex applications. This tutorial shows you how to enhance object recognition in SmartBear TestComplete and eliminate test failures caused by 3D applications, canvas-based apps, and virtualized environments.

Event-Driven Architecture in Modern Banking Systems: Benefits, Use Cases & Implementation Guide

A payment fails. The customer retries. The balance updates late. Fraud detection runs after the fact. This is not a technology gap. It’s an architecture problem. Most legacy banking systems still depend on request-response and batch processing. That means systems wait for data rather than react to it. In a world of real-time payments, instant lending decisions, and always-on digital banking, that delay translates directly into risk, poor customer experience, and operational friction.