Systems | Development | Analytics | API | Testing

Engineering teams want to understand the real health of their applications – not just what was planned or what was tested, but what is actually happening in production. The challenge is that these signals live in different systems, each optimized for a specific part of the delivery lifecycle. Test execution data, issue tracking, and production monitoring each describe a different aspect of system behavior. On their own, they answer narrow questions about validation, delivery, or stability.

Every business running Dynamics 365 Finance and Operations (D365 F&O) relies on the promise of “Evergreen Updates,” which provide seamless, continuous improvement with zero disruption. For a major hospital which was already stretched thin by rising costs and patient loads, the latest update was supposed to be a lifeline, delivering new financial automation features.

In an age where software developers are speeding up their code development to meet the demand of rapid application deployment, there are new tools being developed based on Artificial Intelligence (AI) technology. Replit and Cursor have received a lot of excitement for both of these platforms due to their use of artificial intelligence in assisting developers with coding.

Testing is changing, and AI is leading the next step Every QA team faces the same pressure: test more, deliver faster, and never miss a defect. But as projects grow and release cycles shorten, running every test in every sprint isn’t always realistic. The challenge isn’t just about automating more, it’s about deciding what to test first. That’s where AI comes in.

If your company handles customer payment information, it’s critical for you to understand PCI DSS compliance requirements. A single breach can result in substantial financial penalties and damage your brand's reputation. In my experience working with enterprise customers, I’ve seen firsthand how non-production environments often become a blind spot for compliance efforts.

If you work in a regulated industry like medical device design, automotive engineering, or aerospace, you understand that requirements are the bedrock of product development. Without clear, effective requirements, your team risks compliance failures, scope creep, and costly rework. Requirements engineering is the disciplined application of proven principles, methods, and tools to describe the behavior of a proposed system.

Note: The SmartBear MCP Server is under active development and features may change. Check our GitHub repository for the latest updates and compatibility information. At SmartBear, we show how our MCP Server enables a secure and intelligent bridge between SmartBear platform data and AI-powered development workflows.

Modern teams push code multiple times a day. But how do you verify that the changes made are free from bugs? And how do you integrate testing as part of your pipelines? In this blog, let’s explore how to integrate Keploy into your CI/CD pipelines. We’ll be using GitHub Actions for the examples.

After a year of breakthrough innovation in 2025, Tricentis NeoLoad is headed into 2026 with even bigger goals: more intelligence, more automation, and more speed for performance testing at scale. In the coming year, NeoLoad will continue to provide the advanced foundational features to support effective performance engineering practices as well as intelligent workflows that enable quality and performance teams to work more efficiently than ever before.

Your Perforce IP Lifecycle Management (IPLM) software is a critical asset that benefits from regular management and maintenance. However, managing IPLM can be complex. The scale of deployments can range from a single server to twenty or thirty instances across the globe. An IPLM environment includes a variety of services, such as PI Server, PI Web, PI Cache, Neo4j, Redis, and MongoDB, each with its own set of configurations and log files.