Note
Access to this page requires authorization. You can try signing in or changing directories.
Access to this page requires authorization. You can try changing directories.
In this article, you learn how to use AI-powered actionable insights in Azure Load Testing to identify and troubleshoot performance issues in your application. This feature analyzes your test run data using AI to highlight key issues—such as latency spikes, throughput drops, or backend resource bottlenecks—and provides recommended next steps.
You can view actionable insights directly in the test run dashboard after your test completes.
Prerequisites
- An Azure account with an active subscription. If you don't have an Azure subscription, create a free account before you begin.
- An Azure load testing resource. To create a load testing resource, see Create and run a load test.
- Server-side metrics enabled for your test run. For best results, see Monitor server-side application metrics by using Azure Load Testing
View actionable insights for a test run
To view actionable insights for a completed test:
In the Azure portal, go to your Azure Load Testing resource.
Select Tests, and choose the relevant test run.
Azure Load Testing generates actionable insights on demand. If you're visiting the test run dashboard for the first time, expand the AI summary and insights section, and select Generate insights.
Tip
For the best insights, configure server-side metrics. The AI engine correlates client-side and server-side data to generate more accurate diagnostics and recommendations.
The service generates insights and displays a summary and key insights in the same section. To explore further, select View detailed insights.
In the detailed insights view, you can explore what went wrong during the test, supporting evidence, and recommended next steps.
Caution
AI-generated insights might not always be accurate. We recommend reviewing the evidence and validating with your application’s telemetry