Generate test cases with AI

Accelerate your test case generation by using AI to generate comprehensive test cases directly from your requirement documents, designs, or issue trackers. BrowserStack’s AI agents analyze your inputs to create detailed steps, preconditions, and expected results quickly.

How to generate test cases

You can generate test cases in a single flow, regardless of whether your requirements are in a PDF, a Jira ticket, or a Figma design.

You have now successfully generated a comprehensive set of test cases linked directly to your requirement, ensuring your testing is aligned with your project goals.

Now, you have successfully generated and added AI-powered test cases with detailed test case details to your test case repository, tailored to your specific requirements and context.

The following image shows an example of an AI-generated test case along with its details.

Persistent background generation

Once you start, the AI test case generation runs in the background. You can safely close the generation window or navigate to other parts of the application without interrupting the process. The job is tied to your current browser session and will only stop if you refresh the page or log out. To check the progress, simply reopen the Generate with AI modal.

External context sources

External context sources enable you to seamlessly integrate requirement and design data from third-party platforms into your testing workflow. By connecting these sources, you can associate Figma frames, Jira issues, or Confluence specifications with your test cases, ensuring complete traceability from design to execution. Refer to the table below to understand the supported integration types and their primary use cases.

Document limitations

BrowserStack AI performs best with PRDs that are well-structured, detailed, and unambiguous. For optimal performance, adhere to the following PRD constraints:

If your document exceeds the size or page limit, the upload will fail. To proceed, you must split the document into smaller parts or compress its content before uploading.

Tool-level limitations

To ensure platform stability and consistent performance across all workspaces, the system enforces specific technical boundaries. These constraints define the maximum allowable values for data volume, and concurrent processes. Review the following table to ensure your automation suites and integrations operate within these supported thresholds.

Example of an AI-generated BDD test case

The following image shows an example of an AI-generated BDD test case along with its details.

Traceability and source retention

• Each generated test case stores the original Jira issue or Confluence URL as a reference attribute.
• The link remains clickable, so you can jump back to the exact source page at any time for clarification or updates.
• If the Jira issue or Confluence page changes later, you can re-run generation with the same link to produce updated or supplemental coverage.

Best practices

• Provide the most specific page (avoid large index/parent pages with broad, unrelated content).
• Pair a concise guiding prompt with the link if the page is lengthy or covers multiple modules.
• After major edits to the Confluence page, regenerate or manually review impacted test cases to keep them aligned.

Limitations

• Private or restricted pages require that the authenticated Atlassian user has at least view permission.
• Very large pages may be truncated to a maximum processed size (system automatically prioritizes structured sections first).
• Embedded diagrams or images are not semantically parsed yet—add clarifying prompt text if critical flows are only in images.

This option streamlines context ingestion and strengthens end-to-end traceability between requirements and generated test cases.