AI Bug Severity Suggestion

AI Bug Analysis

Analyses impact, scope, data loss, workaround availability and user exposure to recommend a severity level and explain it against a five-level rubric.

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 makes a good bug report?

A good bug report lets someone else reproduce the problem without asking: a specific title, numbered steps, the expected and the actual result, the environment (build, browser, device, account), data used, and evidence such as a screenshot or log. Severity describes the impact on the system (a crash, data loss); priority describes how soon the business wants it fixed — the two are set separately and often differ.

Read the guide: How to Write a Bug Report

How to use AI Bug Severity Suggestion

  1. Paste the bug report — steps, expected and actual result, and who is affected.
  2. Optionally add product context: how many users are affected, how important the feature is, where the release stands.
  3. Pick the output language and press Suggest severity.
  4. Read the factor-by-factor assessment and the borderline alternative, and answer the questions to confirm before you set the severity in your tracker.

AI Bug Severity Suggestion features

  • Five-level rubric: Blocker, Critical, Major, Minor, Trivial
  • Factor-by-factor assessment (functional impact, data integrity, security, scope, workaround, frequency…)
  • Confidence score and borderline alternative
  • Questions to confirm when information is missing

AI Bug Severity Suggestion example

Severity

Input:

Discount code applied twice → NaN total, payment blocked, reload restores

Output:

Critical (80% confidence): payment blocked for all customers using codes; workaround exists but is not discoverable

Frequently asked questions about AI Bug Severity Suggestion

What is the difference between severity and priority?

Severity measures technical and user impact; priority measures business urgency. Use the companion Bug Priority Suggestion tool for the second.

Why is the confidence sometimes low?

When the report lacks what the decision depends on — how many users are affected, whether a workaround exists, whether data is lost — the tool lowers the confidence and lists the questions that would settle it.

What is the borderline alternative?

When the evidence sits between two levels — Major or Critical, say — the tool names the other candidate and the condition that would change the verdict, such as data being lost or most users being affected.

Technical notes

The result is one of Blocker, Critical, Major, Minor or Trivial with a 0–100 confidence, a justification, and an assessment of each relevant factor — functional impact, data integrity, security, scope of users, workaround availability, frequency, compliance, financial impact, reputation — weighted High, Medium or Low. When the level is borderline it names the other candidate and the condition that would change it.

The levels are checked from the top and the first that fits applies: Blocker — a core flow cannot be completed and there is no workaround; Critical — data loss or corruption, a security exposure, a failed payment or a major function broken for most users; Major — an important function is broken but a workaround exists, or few users are affected; Minor — a small functional, cosmetic or edge-case defect with low impact; Trivial — a typo or a negligible cosmetic detail. The AI Bug Description Generator uses the same rubric.

References