Systems | Development | Analytics | API | Testing

Nowadays, efficient and integrated testing tools are essential for delivering quality quickly. Forrester's 2025 Total Economic Impact of Xray report illuminates how Xray helps organizations streamline testing processes, improve collaboration, and gain significant cost savings.

You may have noticed that the phrase “Let’s take that offline” is gradually being replaced by “Let’s connect async.” Both expressions are a type of white flag, surrendering to the reality that a tricky issue needs to be resolved in a private conversation rather than in a group call. It’s often music to the attendees’ ears because it means the meeting is almost over.

Oracle Cloud applications offer powerful capabilities, but testing them effectively is far from plug-and-play. Many organizations stumble into costly pitfalls during implementation due to the complexity of their business processes, gaps in test coverage, and inadequate planning. Based on our extensive experience in Oracle Cloud Testing, here are the top 5 pitfalls we’ve seen – and how you can avoid them.

Effective quality assurance in Oracle Cloud demands a structured, module-specific automation strategy. From ERP to HCM and CX, aligning test automation to business functions can accelerate test coverage, improve defect detection, and optimize costs.

In today’s fast-evolving Oracle Cloud environments, organizations must strike a balance between agility and compliance. While agility calls for rapid adoption of Oracle Cloud updates, evolving business processes, and dynamic integrations, compliance demands strict adherence to organizational policies, governance standards, and regulatory requirements. Quality Assurance (QA) bridges this divide—enabling innovation while managing risk.

In today’s fast-paced development environment, automatically generating tests has become a necessity. Tools leveraging AI, manual interventions, and live environment captures facilitate this process. For instance, k6 focuses on load testing, generating automated tests with minimal maintenance. On the other hand, AI generative tools like GitHub Copilot, Bard, and ChatGPT can produce many tests, though often with redundancy.

Building AI agents is the first step, and it’s positive to see enterprises exploring this avenue. But it’s only the first step. For true enterprise value, these agents must seamlessly connect to your data ecosystem through robust integration, standardized protocols, and be guided by knowledgeable data teams. The need to give AI agents access to data and connect them to the necessary tools and functions has led to the creation of the Model Context Protocol (MCP).

Almost every web application is built for end-users to view and use. The application deployment process is an important operation in software development because it allows software to fulfill its purpose. This tutorial is primarily for software companies and developers to aid them in deploying and scaling web applications and services using a platform that automatically handles deployment with ease. Therefore, in this tutorial, we will be deploying a Django application to AWS.

MTConnect simplifies how manufacturing machines communicate, ensuring seamless data exchange across systems. It uses HTTP and XML to create a standard data model that works for both old and new equipment, making integration in factories straightforward.

Qlik Connect this year started a little differently. Not with slides. Not with a video. But with a few minutes of unified harmony. On stage stood the Barcelona Guitar Quartet — except instead of four guitars, there was only one. And yet the sound that emerged was rich, layered, and astonishing. If you had closed your eyes, you’d have sworn it was a full band. It was a masterclass in collaboration.