How to use AI Risk-Based Test Generator
- Open the tool and paste the feature or release scope.
- Optionally add context (feature description, acceptance criteria, environment) — the more concrete the input, the better the result.
- Pick the options (count, output language, focus) and press the action button.
- Review the structured result in the table, then copy or download it as Markdown, CSV or JSON.
AI Risk-Based Test Generator features
- Risk register with likelihood × impact scoring and priority
- Targeted tests per risk with objective and type
- Recommended execution order for time-boxed testing
- Markdown, CSV and JSON export
- Choose English, Arabic or Hindi for the generated text — identifiers, HTTP methods and technical tokens always stay in English.
AI Risk-Based Test Generator example
Release → risk plan
Input:
Release 4.2: new payment flow with saved cards, migrated customer DB, redesigned mobile checkoutOutput:
R-1 Payment tokenisation failure (High × High = 9, P0) → tests RT-1…RT-3
R-2 Data loss in migration (Medium × High = 6, P0)
…Frequently asked questions about AI Risk-Based Test Generator
How is the risk score computed?
Likelihood and impact are each rated Low (1), Medium (2) or High (3); score = likelihood × impact (1–9). Risks scoring 6 or more become P0/P1 in the execution order.
Is my input stored?
No. Your text is sent to the Mutqan AI service and the structured result is returned to your browser. We do not store prompts or outputs — only anonymous usage counters used for daily limits.
How many generations can I run?
Anonymous visitors get a small daily allowance per network address; registered users get a higher daily quota. The remaining count is shown under every result.
Can I get the result in Arabic or Hindi?
Yes — choose the output language. Natural-language fields are written in that language while identifiers, HTTP methods, paths and other technical tokens stay in English.
Technical notes
Your input is sent to the Mutqan AI service over an encrypted connection; prompts and results are never stored — only anonymous usage counters used for rate limiting.
The AI must answer with strict JSON that is validated against a schema on the server before anything is rendered; malformed answers are retried once and then rejected with a clear message.