AI Test Data Recommendation Generator

AI Test Generation

Describe fields and rules; the AI proposes concrete test values per field — valid, invalid, boundary, empty, localisation and security probes — with the reason for each and the result the system should give.

Privacy: Requires a free Mutqan account. Your input is sent to the Mutqan AI service to generate the result; prompts and results are not stored — only your account's usage counts for the daily allowance. How the AI tools work

How should AI-generated test artefacts be used?

A language model is good at drafting: turning a requirement into candidate test cases, a stack trace into a readable bug title, or a checklist into a starting point. It does not know your product, so every draft is reviewed by an engineer for correctness, duplicates and missing negative paths before it enters a test plan — the same rule as for any junior contributor.

Security note: Requirements, tickets and error messages pasted into an AI tool are sent to the model. Remove customer data, credentials and internal hostnames first; the output is a draft to review, not a decision.

How to use AI Test Data Recommendation Generator

  1. Paste a feature description or a list of fields with their rules (type, required, length, format).
  2. Choose how many values you want per field (2 to 8) and the output language.
  3. Press Generate and check the detected fields — each one shows the rules the tool inferred.
  4. Use the recommended values as test inputs and download them as CSV, Markdown or JSON.

AI Test Data Recommendation Generator features

  • Detects fields and rules from a description or field list
  • Valid, invalid, boundary, empty, localisation, security and format values per field
  • Expected result for each value so it doubles as a test case list
  • Pairs with the Test Data Factory when you also need data in volume

AI Test Data Recommendation Generator example

Fields → data

Input:

email: required, max 254
password: 10–128, letters + digits

Output:

email valid: qa.user+tag@example.com
email boundary: 254-character address
password invalid: 9 characters → "at least 10 characters" error
…

Frequently asked questions about AI Test Data Recommendation Generator

Can I turn the recommendations into a large dataset?

The recommendations are hand-picked values that each test one rule. When you also need data in volume, the Test Data Factory generates up to 1,000 realistic rows in JSON, CSV, SQL or XML, and the dataset generators up to 5,000 CSV or SQL rows or 50,000 JSON records.

Which boundary values does it suggest?

It applies boundary value analysis to each limit: the limit and its nearest neighbour outside it (min−1, min, max and max+1, the two-value form), plus the nearest value inside it (min+1 and max−1) for the three-value form. With only a few values per field it keeps the boundaries most likely to reveal a defect, and says what the system must do with each.

Does it include security and localisation values?

Yes, where a field accepts free text: injection strings and script tags as security probes, and Arabic, Hindi, right-to-left text, emoji and accented characters for localisation — each with the result the system should give.

Technical notes

Each recommendation has an ID (TD-01, TD-02 …), the field it belongs to, a category (valid, invalid, boundary, empty, localisation, security, format or special characters), the literal value to type, why it matters and the expected result — accepted, rejected with which message, or stored as what. Arabic, Hindi and emoji values are given as real characters, not descriptions.