How to use AI Test Case Optimizer
- Open the tool and paste your existing test cases.
- 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 Test Case Optimizer features
- Parses any format (tables, lists, Gherkin, prose) into structured cases
- Atomic steps, explicit data, complete preconditions, observable expected results
- Splits compound cases and merges duplicates without losing coverage
- Change log with reasons and before/after quality scores
- Choose English, Arabic or Hindi for the generated text — identifiers, HTTP methods and technical tokens always stay in English.
AI Test Case Optimizer example
Before → after
Input:
1. Delete lookup value - delete worksOutput:
TC-001 Admin deletes an unreferenced lookup value — steps 1–2, expected: success toast, value removed from list and APIFrequently asked questions about AI Test Case Optimizer
Will it change what my tests verify?
No. Coverage is preserved; only clarity, structure and precision improve. Merges and splits are listed in the change log so you can review them.
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.