AI Workspace Demo Challenge

​Good day ​@Mustafa ,

Here is my response below,
 

From an Ambiguous Requirement to Testable Scenarios Using AI Workspace

I would use AI Workspace as a collaboration assistant between the Product Owner and QA—not simply as a test-case generator.
 

Example: E-commerce Discount Code

Initial requirement:

“Users should be able to apply a discount code during checkout.”

This requirement is not fully testable because the business rules, restrictions and expected error behaviour are unclear.

1. Analyse the Requirement

I would enter the requirement into AI Workspace with the following prompt:

“Analyse this requirement and identify ambiguities, missing business rules, boundary conditions, negative scenarios and clarification questions for the Product Owner. Do not generate test cases until the requirement is confirmed.”

AI Workspace could identify questions such as:

• Can multiple discount codes be applied?

• Is there a minimum order value?

• Is the code case-sensitive?

• Can expired or inactive codes be entered?

• Can the same customer reuse the code?

• Are any products excluded?

• Can the discount exceed the order value?

• What message should appear when a code is rejected?

2. Refine It with the Product Owner

During the refinement discussion, the Product Owner confirms:

• Only one discount code can be applied per order.

• The minimum order value is ₹1,000.

• Codes are case-insensitive.

• Expired, inactive and already-used codes must be rejected.

• Restricted products are excluded.

• The discount cannot exceed the eligible order value.

• The customer must receive a clear validation message.

I would enter these decisions back into AI Workspace and ask it to rewrite the requirement as measurable acceptance criteria.

3. Create Testable Acceptance Criteria

Example:

Given the customer has eligible products worth ₹1,000 or more in the cart
And the discount code is valid and active
When the customer applies the code
Then the correct discount must be applied to the eligible products
And the updated order total must be displayed.

The Product Owner reviews and approves the refined requirement before test-case generation begins.

4. Generate Well-Defined Test Cases

I would prompt AI Workspace:

“Generate positive, negative, boundary and business-rule test cases strictly from the approved acceptance criteria. Include Test Case ID, preconditions, test data, steps, expected result, priority and acceptance-criteria mapping.”

5. Apply Human Review

Finally, I would use AI Workspace to:

• Check that every acceptance criterion has test coverage.

• Identify missing, duplicate or contradictory scenarios.

• Highlight any test case based on an unapproved assumption.

• Map each test case to its acceptance criterion.

• Suggest suitable regression and automation candidates.

The Product Owner remains responsible for the business intent, while the QA engineer validates coverage, test data, risk and feasibility.

Workflow:

Raw requirement → AI gap analysis → PO clarification → Approved acceptance criteria → Test-case generation → Traceability → Human review

AI Workspace does not replace the Product Owner or QA engineer. It helps them ask better questions, remove ambiguity earlier and create complete, traceable and high-quality test cases.

The best use of AI in testing is not generating more test cases it is ensuring that we are testing the right requirement.

Thanks,

Ramanan

• Understand the Requirement: Use AI to summarize the business requirement and identify unclear or missing details.
• Collaborate with the Product Owner: clarification questions to validate assumptions and refine the requirement.
• Define Acceptance Criteria: Convert the finalized requirement into clear, testable acceptance criteria 
• Identify Risks and Edge Cases: Ask AI to suggest negative scenarios, boundary conditions, and potential business risks.
• Generate Test Cases: Create functional, negative, boundary, regression, and security test cases with clear steps and expected results.
• Maintain Traceability: Map requirements to acceptance criteria and test cases to ensure complete test coverage.

Create Agentic AI using AI Workspace 

I use AI Workspace as a requirement-refinement and test-design assistant. First, I provide the raw requirement and ask AI to identify ambiguities, missing business rules, edge cases, and questions for the Product Owner. After discussing and clarifying those points with the PO, I update the requirement with the agreed acceptance criteria. Then I use AI to derive positive, negative, boundary, and security scenarios and generate structured test cases with preconditions, steps, and expected results. Finally, I manually review the AI-generated test cases against the approved requirement to ensure accuracy and coverage.

Thank you everyone for participating in this challenge! And we are happy to announce the winner ​@jijomathai 🎉
I will get in touch with you to organize the prize delivery:)