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For more detailed information on working with sprints in Jira, check out the sprints tutorial.
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This tutorial depends on several components that interact with each other. In this tutorial, you will:
1. Configure Jira Automaton to create a Dynatrace problem card comment with the URL back to the Jira issue.
2. Configure Dynatrace to push problems to Jira.
3. Trigger a problem in the sample application and review how Dynatrace detects the problem and automatically creates a Jira issue.
4. Configure Jira issues queries within the Dynatrace Release page.
5. Clean up your environment.
This diagram shows the components for this tutorial and the basic interaction between them.
1. Monitor application with Dynatrace – For this tutorial, Dynatrace will collect metrics from a Dynatrace agent installed on the host that runs the sample application. All the data is centralized into the central Dynatrace tenant, which also provides the web interface for administration and AI-powered problem detection.
2. Problem notification with detailed context – Dynatrace’s AI Engine, Davis, uses high-fidelity metrics, traces, logs, and real user data mapped to a unified entity model. It uses Davis’ deterministic AI to reveal the precise root causes of problems. Davis not only locates the precise root cause, but it delivers valuable context instantly. You'll know whether a problem is the result of a resource bottleneck or deployment change, and even who's behind it. You can replay problems to fully understand why they happened and how to fix them.
3. Automatic creation of a Jira issue – The Dynatrace and Jira integration automatically issues for all new problems auto-detected in your Dynatrace environments.
4. Jira automation updates Dynatrace – Automation in Jira is a “no-code” feature that allows for easy build rules in a few clicks. A web request to the Dynatrace API uses the Dynatrace problem ID to update the Dynatrace problem card comments.
5. Resolve problem and close Jira issue