Systems | Development | Analytics | API | Testing

Software testing at scale comes with unique challenges. Large organizations manage thousands of test cases across multiple projects, often under strict compliance standards and within fast-moving CI/CD pipelines. Until recently, Xray Enterprise existed as an add-on to the main Xray Test Management for Jira app, requiring customers to install and manage both solutions. This created confusion around licensing and setup, and made it harder for teams to understand what they were really getting.

Have you ever stared at a section of code and felt lost, even though it seemed straightforward at first glance? Many developers have faced this challenge – where understanding, testing, or modifying code feels harder than it should. This difficulty is what we refer to as code complexity, a factor that directly affects software quality, maintainability, and performance.

Software testing at scale comes with unique challenges. Large organizations manage thousands of test cases across multiple projects, often under strict compliance standards and within fast-moving CI/CD pipelines. Until recently, Xray Enterprise existed as an add-on to the main Xray Test Management for Jira app, requiring customers to install and manage both solutions. This created confusion around licensing and setup, and made it harder for teams to understand what they were really getting.

As AI-powered agents begin to play a more active role in quality engineering, regulated industries especially in BFSI, healthcare, and government face a critical question: How do you scale agentic QA without losing control, visibility, or auditability? This blog explores how to make agent-augmented QA compliant, explainable, and trustworthy, not just in spirit, but in process, tooling, and evidence. We’re not talking about testing AI systems.

Drone technology — also known as unmanned aerial vehicle (UAV) and unmanned aircraft system (UAS) technology — is expanding rapidly across commercial, industrial, and defense sectors. As these systems become more complex, they increasingly rely on sophisticated software for aerial embedded systems.

2025 will be remembered as the year AI wrote poetry, passed medical exams, and stumbled with spectacular blunders. For every impressive breakthrough, there was an equally impressive facepalm.