QA for gaming Part 2: Phases and the Value of Crowdtesting
Many engineering leads assume game testing happens only near launch, with testers logging bugs in the final months.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
The latest News and Information on Software Testing and related technologies.
Stop Chasing Ghosts: How AI Helps Solve Flaky Test Detection
Flaky tests not only slow your pipeline but also limit how fast your entire organization can move. Learn how AI is finally breaking through it.
How to Build a Digital Wallet: Architecture, Security, and Compliance Guide
How to build a digital wallet is not a UI problem. It is an infrastructure problem, one with PCI-DSS scope, secure-element dependencies, card network certification, and licensing obligations baked in from day one.
How Enterprise Teams Are Keeping Up With AI-Generated Code at Scale | Perforce 2026
When AI Starts Shipping Code: Managing the Collision Between Human and AI-Generated Code AI agents don't wait for reviews. They generate code overnight, work across the same codebase in parallel, and produce more changes than any human team can realistically process — creating a new kind of bottleneck we call the Merge Wall. In this session, Perforce engineering leaders break down what happens when human and AI-generated code collide at scale — and how leading teams are building the visibility, governance, and coordination layers required to keep up.
TestComplete vs. Reflect: Which SmartBear test automation platform fits your team?
Not every test automation problem looks the same. A team maintaining complex desktop applications in a controlled financial services environment has different automation needs than a team shipping web and mobile updates every two weeks. The application, the environment, and the people creating tests all shape what “good automation” has to do.
From testing to trust: Why quality engineering is becoming the control plane for AI driven enterprises
Enterprises are under pressure to deliver software faster without sacrificing trust. AI generated code, continuous delivery, and increasingly agentic systems are accelerating change faster than traditional quality practices can validate it. For enterprises running multi-layered tech stacks, weekslong regression cycles and performance issues that are discovered by customers in production are symptoms of a behind-the-scenes quality model that was built for a slower era.
Tricentis qTest + Vera Integration Setup | Automated Digital Validation
Learn how to connect qTest Manager, a component of Tricentis qTest, with Tricentis Vera (Validated Electronic Record Approval) to achieve compliance through electronic signatures and workflow controls. In this quick setup walkthrough, you'll see how to: Navigate to Integration Settings within qTest Manager Select and configure the Vera external system integration Enter your Vera callback URL to link the two platforms.
Are painless quarterly Oracle updates closer than we think?
Quick overview: Oracle’s Fusion quarterly update cycle has always been a pressure test for QA teams, but agentic AI automated testing may be changing that. Self-healing tests, natural language test creation, and context-aware agents are giving teams new ways to absorb Oracle’s pace of change without the usual scramble. As Oracle’s own AI capabilities make each release more complex, the tools designed to test AI-driven outcomes will matter more.
Sponsored Post
The Kubeshark Workflow That Doesn't Stop at the Dashboard
The Observability Gap shows up the moment you try to reproduce a production bug locally. Your traces tell you a request was slow. Your logs tell you which line printed. Neither tells you what was actually on the wire: the headers, the JSON body, the surprise field your client started sending last Tuesday. Until now, closing that gap meant SSHing to a node, attaching a debugger, or shipping a sidecar through change review.
