Smarter Planning from Day One: How TestRay Uses AI to Generate Requirements in Jira Data Center

In development environments, one of the biggest bottlenecks is clarity or rather, the lack of it. Projects often begin with vague user stories or scattered inputs, leaving QA and dev teams scrambling to translate intent into structured, actionable work items. That’s where AI-powered requirement generation in TestRay comes in.

What TestRay's AI Requirement Generation Does

TestRay’s Requirement Generation feature turns raw inputs like notes, docs, or even a Confluence page into fully structured Jira requirements using AI. It helps teams move from fuzzy ideas to concrete requirements with far less manual effort.

It’s like having a junior analyst who can take what you’re thinking and format it into something your team can actually use.

How TestRay's AI Requirement Generation Works

You can launch the feature directly inside Jira by navigating to:

Jira Project → Requirements → Requirement Generation

From there, you feed the AI with one or more of the following inputs:

Input Type	How It Works
Confluence Page(s)	Enter a URL to any linked Confluence page. The AI will parse the contents to generate Jira requirements.
Additional Instructions	Paste in freeform text like “We need to handle expired logins for returning users.”
Documents	Upload PDFs or .txt files with specs, user stories, or internal documentation.

You can use just one input — or combine them all to provide richer context.

Review the AI Requirement Generation Before You Commit

Once you click "Generate", the AI processes your input and displays a list of proposed requirement issues, complete with summaries.

You can:

• Review the content

• Select which ones you want to keep

• Edit them before finalizing

• Hit “Create” to push the selected requirements into Jira

No surprises. You’re always in control.

Why AI Requirements Generation Matters

Writing requirements is time-consuming and more often than not, uneven in quality.

Sometimes you get clear bullet points. Sometimes it’s a Slack message that says “make it smarter.” Either way, someone has to translate that into something usable.

TestRay’s AI Requirement Generation helps teams:

• Kickstart planning without starting from scratch

• Standardize structure across teams or projects

• Reduce the back-and-forth between PMs, analysts, and QA

Real-World Use Case of AI Requirements Generation

Imagine your product team adds a new epic about secure guest checkout. They’ve jotted some notes in Confluence. A dev has a draft spec on their machine. You want to start building but first, you need requirements. Instead of copying, pasting, and rewriting, you feed those notes and docs into TestRay AI. In seconds, you have a dozen structured requirements to review, edit, and assign — all traceable and all inside Jira.

TestRay's Requirements + Storm AI

To use this feature, you’ll need:

• TestRay 12.0 or above

• The Storm AI for Jira app installed and configured

• An account with an AI provider like OpenAI, Google Gemini, Vertex, or Azure OpenAI

Storm AI acts as the secure connector that enables TestRay’s intelligent features — without storing or training on your data.

[Watch a quick walkthrough of TestRay's AI features enabled by Storm AI.]

Takeaway

AI isn’t replacing human thinking — it’s helping you get to the thinking part faster. With TestRay’s Requirement Generation, you spend less time formatting issues and more time defining the right work to build the right things. Want to learn more about TestRay's AI features and see it in action? Book a demo or Request access to a trial version.