Systems | Development | Analytics | API | Testing

qTest Manager Explained: Test Plans to Execution Reports in less than 3 minutes

Get a quick walkthrough of qTest Manager by Tricentis—the test management platform built for modern QA teams and developers. In this video, you'll see how qTest Manager is structured around four core components: Test Plan – Set up and organize your projects with timelines, releases, and version tracking Requirements – Manage and track requirements directly within your QA workflow Test Design – Build and organize your test case library.

Test Execution & Defect Reporting in qTest Manager | Full Walkthrough

See exactly how QA testers execute manual test cases and report defects directly from qTest Manager—all in one seamless workflow. In this demo walkthrough, you'll see: Test Execution View – Navigate test suites, review test run properties, and launch execution via TestPad Step-by-Step Execution – Walk through individual test steps, log actual results, and mark steps as Passed, Failed, Blocked, or Skipped in real time.

SmartBear at Atlassian Team '26: A Recap of What's New with AI and Rovo

What did Atlassian Team ’26 reveal about the future of software quality and AI-powered delivery? In this recap from the event floor inside the Anaheim Convention Center, SmartBear shares key themes from the event, including Atlassian Rovo, the Teamwork Graph, AI-driven workflows, and how QA teams are adapting to faster, AI-assisted software delivery inside Jira. See quick highlights from the event floor, SmartBear’s latest Zephyr innovations, and how conversational AI and quality intelligence are becoming part of the modern software delivery workflow.

Integrating RAG and GenAI into Customer 360 Architecture

Traditional Customer 360 architectures were perfectly adequate for the era of quarterly reports and static marketing segments. They successfully pooled data from CRMs, transaction logs, and support platforms to build a unified profile. But for GenAI-powered applications? Yesterday's architecture is a massive bottleneck. Here is why legacy systems are breaking down under the demands of modern AI, and how the architecture is forcing a shift to real-time data.

Confluent Cloud: Making an Apache Kafka Service 10x Better

People often imagine that to provide a cloud service for a piece of open source software is a simple matter of packaging up the open source and putting it in Kubernetes. We knew when we set out to build Confluent Cloud that a true cloud-native offering of Apache Kafka as a service would be much, much more than that.

Stateful vs. Stateless Web App Design | DreamFactory

Last updated: May 2026 Stateful applications remember information about previous client interactions. Stateless applications treat every request as independent — no memory between calls. The choice between these two designs shapes how an application scales, how it handles failures, and increasingly how AI agents consume it.

Key Integrations Required in a Modern Hospital Management System: EHR, LIS, RIS, Pharmacy, Billing & Beyond

That gap is exactly where inefficiency begins. A modern hospital management system is no longer just about digitization. It is about connection. Without the connections, hospitals face significant hurdles in patient safety and data integrity. Integration is what transforms a collection of tools into a working healthcare ecosystem. When key integrations in a hospital management system are done right, everything changes. Data flows without friction. Clinicians make faster decisions.

Here's the Jira Data Center Alternative You're Looking For

Atlassian recently announced end of life for all their Data Center products, including Jira Data Center. That means every studio must evaluate and choose a new planning tool by Atlassian’s planned sunset date, March 28, 2029. If you’re looking for a new on-premises solution—because cloud options aren’t viable for your team—this blog explains how P4 Plan can meet, and often exceed, what Jira Data Center and Jira Cloud offer now.

Are Microservices Dying?

LLMs are absorbing the business logic of microservices for agentic use cases — but both patterns will coexist in enterprise infrastructure for a long time. Cloud-native infrastructure (microservices + APIs) keeps powering web and mobile experiences. The agentic layer — LLMs, MCP tool calls, and context traffic — runs in parallel, activating the same APIs and CRUD operations underneath. Kong manages both swim lanes: the API traffic between clients and microservices, and the context traffic flowing between agents and LLMs.#Shorts.