Unlock Xray’s full potential with Xray Academy — role-based courses to master testing, automation, and reporting at scale.
Discover Xray App Editions for Jira Cloud — flexible plans that scale with your team, from Standard to Advanced to Enterprise.
Validate AI with AI. Explore smart strategies to test machine learning models for bias, drift, and transparency—keeping humans in control.
Discover Xray’s new AI-powered testing features — AI Test Case Generation and AI Test Model Generation, powered by Sembi IQ. Faster, secure, human-validated testing built directly in Jira.
Accelerate test modeling with Xray AI Test Model Generation. Transform requirements into models and expand coverage with confidence.
Learn best practices for adopting AI in QA. See how AI supports test case creation, analysis, and strategy without replacing testers.
Save time and cut errors with Xray’s AI Test Case Generation now available in Beta. Review, edit, and generate quality tests that match your workflow.
Exploratory testing has always thrived on adaptability, critical thinking, and creativity — qualities that AI cannot replace. Instead of making exploratory testing obsolete, AI enhances it: fueling testers with context, guiding exploration with risk-based insights, and automating repetitive tasks so humans can focus on discovery.
Integrate performance testing into Agile with Xray for faster releases, better user experience, and full Jira traceability.
Understand how Xray’s Requirement Coverage brings real-time visibility to your testing efforts. By connecting test executions to Jira requirements, teams can automate traceability, reduce release risks, and ensure compliance in fast-paced environments. Learn how to set it up, avoid common pitfalls, and integrate coverage insights directly into your CI/CD workflows.
This article explores how AI can act as a supportive assistant, helping testers prepare sessions, spot patterns, and organize insights while leaving the critical thinking to humans. You’ll also discover practical tips and best practices for integrating AI assistants like ChatGPT into your testing workflow.
Too often, software testing metrics are reduced to pass/fail numbers or bug counts. But if you're only looking at those, you're missing the bigger picture. This article explores how QA metrics can be used strategically — not just to track performance, but to connect your team’s work directly to business outcomes. Learn which metrics matter most (like defect leakage and requirements coverage), how to use them to make better decisions, and how tools like Xray can support your strategy.
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