AI Risk-Based Test Generator

AI Test Generation

Provide the feature/release scope and context; the AI lists risks with likelihood/impact, prioritises them and proposes targeted tests per risk.

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

What is regression testing, and what does risk-based testing add?

Regression testing re-runs existing tests after a change to confirm that what used to work still does. Because a full suite grows beyond what any release window allows, risk-based testing ranks features by the likelihood and the cost of failure — payment, sign-in, data integrity first — and decides what runs on every build, what runs nightly and what is automated. A regression suite is the natural first target for automation.

Read the guide: How to Write Test Cases

How to use AI Risk-Based Test Generator

  1. Describe the feature or release scope: what is new or changed, and who uses it.
  2. Optionally add context — architecture, known weak areas, past incidents, deadlines — and choose how many risks to analyse (3 to 20).
  3. Pick the output language and press Generate.
  4. Work through the risk register in the recommended execution order, then download it as CSV, Markdown 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

AI Risk-Based Test Generator example

Release → risk plan

Input:

Release 4.2: new payment flow with saved cards, migrated customer DB, redesigned mobile checkout

Output:

R-01 Payment tokenisation failure (High × High = 9, P0) → tests RT-01…RT-03
R-02 Data loss in migration (Medium × High = 6, P0)
…
Likelihood × impact on a 1–3 scale allows only six scores; 6 or 9 is P0, and the highest score runs first, so R-01 (9) comes before R-02 (6).
LikelihoodImpact
Low1Medium2High3
High33P16P09P0 · R-01
Medium22P24P16P0 · R-02
Low11P32P23P1
  • P0: score 6 or 9
  • P1: score 3 or 4
  • P2: score 2
  • P3: score 1
  • R-01Payment tokenisation failure: likelihood High × impact High = 9, P0
  • R-02Data loss in migration: likelihood Medium × impact 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), and the score is likelihood × impact, so the only possible scores are 1, 2, 3, 4, 6 and 9. A score of 6 or 9 is P0, 3 or 4 is P1, 2 is P2 and 1 is P3.

Are the tests generic smoke tests?

No. Every test belongs to one risk and states what it must prove, with the key data or condition — for example that a saved-card payment fails safely when tokenisation times out.

What is the mitigation column for?

Advice that is not a test — monitoring, a safeguard, a rollback plan — for risks that testing alone cannot remove.

Technical notes

Each risk has an ID (R-01, R-02 …), a category, a description, a likelihood and an impact (Low, Medium or High), a score, a priority, a mitigation or monitoring note, and up to six targeted tests (RT-01, RT-02 …, unique across the register), each with the objective it must prove. The execution order lists every risk once, highest score first.

References

  • ISTQB Certified Tester Foundation Level (CTFL) v4.0ISTQB · 2023The foundation syllabus of software testing: test levels and types, test design techniques, test management and tools.
  • ISTQB GlossaryISTQBThe standard definitions of testing terms — test case, regression testing, severity, priority, boundary value analysis and the rest.