Functional Overview

Issue AI Analytics for Jira introduces new analytics functionality for Jira issues. It exposes an Enable AI Analytics button in the Issue View screen. Once button is pressed, the issue summary and description are sent for analysis to an external AI engine. The analysis results are displayed as tabs, through a new panel in the Issue View screen.

Example:

The tabs that are displayed and the analysis that is done by the AI engine is configurable, depending on the needs of your organization (reference Configuration section).

The following analytics tabs are available by default:

• Instructions – Instructions with proposals and/or concrete solutions on how to solve tasks. It might contain steps to follow, general advise on how to approach the problem or in some cases, a direct solution. This section will become more accurate, as more detailed issue description is provided.
• Description Improvements – Suggestions on how to improve clarity of an issue description. In some cases, the issue author might not provide a description that is detailed or clear enough – this section will help improve the quality and make it more understandable.
• Definition of Done (DoD) – Most teams don’t spend enough time to write Definition of Done (DoD) for issues. This section will automatically generate the DoD. It will help the author to properly define, when an issue can be considered done.
• Test Strategy – Complete strategy on how to test your issues are successfully completed. In most cases, a comprehensive suite of tests will be provided. This might include tests for non-functional requirements or corner cases, which are easily forgotten.

The following AI engines are supported:

• OpenAI GPT-4o
• OpenAI GPT-3.5-Turbo
• Anthropic Claude 3 Opus
• Anthropic Claude 3 Sonnet
• Anthropic Claude 3 Haiku
• Azure OpenAI Service

Architectural Overview

Issue AI Analytics integrates Jira with an external AI provider for analytics through a Heroku server. This can be visualized as follows:

The Deview Studios app server is hosted on the Heroku platform and is the entry point for the integration.

Integration process triggers whenever the Enable AI Analytics button is pressed in the Issue View screen. Only the issue summary and description are sent for analysis.

First time analytics generation for an issue can take between 20-60 seconds. This is primarily due to the processing time needed by the AI provider. Once generated, analytics are cached in an analytics database and every subsequent display of analytics results in the Issue View screen is immediate. Analytics results will be re-generated, if issue summary or description is changed.

The integration process can be visualized as follows

The analytics database only caches analytics results and never stores issue summary or description. Analytics results are stored fully encrypted in the database. Caching is required to provide better user experience, by not mandating the user to wait 20-60 seconds every time.

Requirements

The following conditions must be met in order to use Issue AI Analytics for Jira:

• N/A

Functional Overview

Issue AI Analytics enhances Jira by seamlessly integrating advanced AI capabilities. The following key features are available to users:

AI Analytics

An Enable AI Analytics button appears on the Issue View screen. When this button is clicked, the issue details are sent to an external AI engine for analysis. The results of the analysis are then presented as tabs within a new panel on the Issue View screen.

Example:

The tabs that are displayed and the analysis that is done by the AI engine is fully configurable, depending on the needs of your organization (reference Configuration section).

The following analytics tabs are available by default:

• Instructions – Instructions with proposals and/or concrete solutions on how to solve tasks. It might contain steps to follow, general advise on how to approach the problem or in some cases, a direct solution. This section will become more accurate, as more detailed issue description is provided.
• Description Improvements – Suggestions on how to improve clarity of an issue description. In some cases, the issue author might not provide a description that is detailed or clear enough – this section will help improve the quality and make it more understandable.
• Definition of Done (DoD) – Most teams don’t spend enough time to write Definition of Done (DoD) for issues. This section will automatically generate the DoD. It will help the author to properly define, when an issue can be considered done.
• Test Strategy – Complete strategy on how to test your issues are successfully completed. In most cases, a comprehensive suite of tests will be provided. This might include tests for non-functional requirements or corner cases, which are easily forgotten.

AI Assistant

A chat-like AI assistant directly into your workflow, allowing you to interact with AI in real-time for issue management. It can help with the following tasks:

• Context-Aware Assistance: The AI Assistant has full access to the context of your issue, enabling it to provide relevant and accurate responses.
• Task Support: Generate solutions, summarize issues, draft responses, prioritize tasks, and more, all within the Jira Issue View screen.

Example:

AI Agents

1. Creation – a Jira system administrator creates an AI Agent, defining its capabilities and automation settings.
2. Project assignment – a project administrator assigns the created AI Agent to the relevant Jira project(s), making it available to project users.
3. Usage – project users can leverage the AI Agent in two ways:

• • Automatic: The AI Agent is triggered automatically by project events (such as issue creation or status change), and performs its configured actions without user intervention.
  • Semi-Automatic: Users can manually activate the AI Agent from a button on the Issue View screen, if enabled, review the AI’s suggested actions, and choose whether to apply them.

Architectural Overview

Requirements

The following conditions must be met in order to use Issue AI Analytics for Jira:

• Connectivity to the desired AI model (HTTP proxy is supported). Firewall access must be enabled from all Jira cluster nodes.
• For Private AI mode – a Private AI server must be available and reachable from Jira

/* MODIFY THIS SCRIPT TO INTEGRATE YOUR AI MODEL. THIS IS JUST AN EXAMPLE */
import com.atlassian.jira.util.json.*
import org.apache.http.entity.StringEntity

def messagesArray = new JSONArray();
if(type.equals("chat")) {
// This is an AI Assistant request
messagesArray.put(new JSONObject().put("role", "system").put("content", issueContext)); // Set AI context
messages.each { messageObj ->
messagesArray.put(new JSONObject().put("role", messageObj.getRole()).put("content", messageObj.getMessage())); // Put all chat messages
}
} else if(type.equals("analytics")) {
// This is an AI Analytics request 
messagesArray.put(new JSONObject().put("role", "system").put("content", issueContext)); // Set AI context
def aiQuery = issueSummary + "\r\n" + issueDescription + "\r\n---\r\n" + query; // Build the query that will be sent for analysis
messagesArray.put(new JSONObject().put("role", "user").put("content", aiQuery));
}
def payload = new JSONObject() // Prepare body as per API specification
.put("model", "gpt-4.1")
.put("messages", messagesArray)
.put("temperature", 1.0)
.toString();
def post = httpClient.newPost("https://api.openai.com/v1/chat/completions"); // Create a new request to specified URL
post.setEntity(new StringEntity(payload, java.nio.charset.StandardCharsets.UTF_8)); // Set HTTP payload
post.addHeader("Content-Type", "application/json"); // Set HTTP header
post.addHeader("Authorization", "Bearer " + crypto.decrypt("HOTqnMRPU6KDhM=")); // Set HTTP header
JSONObject json = new JSONObject(httpClient.sendRequest(post)); // Parse response as JSON
def result = json.getJSONArray("choices").getJSONObject(0).getJSONObject("message").getString("content"); // Traverse JSON tree
return result; // This script must always return the produced result