AI Bug Report Generator: Template and Worked Example

An AI bug report generator can organize rough observations into a report a developer can investigate: a precise title, environment, reproduction steps, expected behavior, actual result and evidence. It needs your observations to do that well. AI cannot establish that a bug reproduced, inspect an attachment you did not supply or turn a guess into a verified cause.

This guide includes a fictional worked report, a free blank bug report worksheet and a local report-brief planner. Use them to separate what you saw from what still needs checking. Preparing the brief does not contact a developer or submit a ticket. A reviewed AI draft is a separate chat request using your Brainbaby account allowance.

What makes a bug report actionable?

A useful report reduces the next person's uncertainty. Name the affected behavior and the condition that triggers it. State which rule defines the expected result, which environment produced the observation and whether a second attempt has confirmed it. An issue title such as “Filter is broken” leaves most of that work for the reader.

Separate these three things throughout the report:

Part	What belongs there	What does not establish it
Expected behavior	An approved requirement, design decision or documented contract	What the reporter wishes the feature did
Actual result	The visible outcome or recorded response from the attempt	A suspected implementation cause
Root-cause hypothesis	A clearly labeled possibility to investigate	A polished AI explanation without evidence

Atlassian's bug-report template includes environment, reproduction steps and expected versus actual results. The worksheet here uses that general reporting discipline with original fields and examples. It is a planning file, not a Jira integration or an automatically created issue.

A complete fictional bug report example

This teaching example continues the workshop request-list scenario from the AI test case generator guide. Everything below is fictional. It is not a Brainbaby defect, a customer incident or a claim that we ran a test. The source rule is R-4: the Pending filter displays pending requests only.

Title

Reviewed item remains visible when Pending filter is selected

This describes the visible failure and its condition. It avoids claiming that a particular framework, database query or cache caused it.

Environment and preconditions

Product: fictional local workshop request-list demo.
Build, browser version and operating system: TO CONFIRM.
Data: item A has state Pending; item B has state Reviewed.
Source expectation: tutorial requirement R-4.
Related manual case: TC-6.
Exact account role and feature flags: TO CONFIRM if relevant.

Reproduction steps to verify

Open the local request list with items A and B present.
Confirm their stored demonstration states are Pending and Reviewed respectively.
Select the Pending filter once.
Inspect the visible request rows.

These are a proposed minimal reproduction sequence derived from the fictional observation. They are NEEDS RERUN, not a record of an executed sequence. Someone still needs to confirm the setup and actions on a named build.

Expected and actual result

Expected: only item A is visible under Pending, based on R-4.

Actual observation supplied in the tutorial: both A and B remain visible after Pending is selected.

Frequency: not measured. Evidence: no screenshot, recording or log supplied. Workaround: not verified. Suspected cause: none established. Severity: provisional, for review; the observation affects filter correctness, but the extent of affected data or users is unknown. Priority: leave for team triage.

This report is useful because it exposes its gaps. A generated browser version, fabricated console error or invented “3 out of 3” reproduction rate would make it look more complete while making it less trustworthy.

How to draft a bug report with Brainbaby

1. Gather the observation before choosing a format

Copy the exact visible error text, if there is any, and write down the actions you remember. Record the current app build, device or browser and the state of the test data. If those facts were not captured, label them missing. Do not recreate a precise history from memory and present it as a recording.

Choose one issue per report. If a save action fails and a filter also shows the wrong rows, splitting them usually makes verification clearer unless evidence shows a single connected failure. Link a related test case or the requirement in your product requirements document so the expected behavior has a source.

2. Supply a bounded, shareable excerpt

The planner's source field accepts up to 5,000 characters. Include the relevant observation, approved expected rule and evidence references within that limit. It is not an automatic session recorder or a full-repository scanner. An evidence reference names material to review; it does not attach the file to chat or prove that the model can access it.

Remove access tokens, cookies, private customer details and unrelated personal information from logs and screenshots before sharing them. Describe account conditions with a safe test role where possible. Keep sensitive originals in your team's approved system rather than embedding them in a public issue.

3. Prepare and review the writing brief

The local form organizes your notes into a request for AI drafting. Check the editable brief before continuing. You can change the output format, restrict it to one report or ask for missing-evidence questions first. Your source text stays out of the continuation URL and interaction analytics.

Continuing preserves an unsent draft in the same browser tab, including through sign-in. You decide when to send it in Brainbaby chat. Sending uses your current account allowance; local preparation and the worksheet use no credits. Review current chat plans and allowances if you need recurring drafting capacity.

4. Check the report against what you supplied

Compare every specific number, version, error string and evidence reference with your notes. Ask the model to remove unsupported details, rather than invent values for empty fields. Check that the actual result describes an observation and that a suggested cause is labeled as a hypothesis.

If the model suggests better reproduction steps, retain a NEEDS RERUN marker until a person executes them. A teammate should be able to distinguish your original attempt from a cleaner proposed sequence. Keep the original evidence accessible to reviewers without publishing unnecessary private data.

5. Copy the approved report into your tracker

After review, manually copy the report into the team's chosen issue tracker. GitHub's issue documentation describes creating an issue and using a repository's available templates. Apply the destination's fields and permissions yourself; this Brainbaby flow does not create or submit an issue on your behalf.

Include the associated test case, owner and evidence reference. Check for a duplicate before creating another issue. If someone fixes the implementation, retain the original observation and add a separate verification record with build, tester, date and result. Editing a report to say “fixed” is not the same as verifying the fix.

Report fields for Jira, GitHub or a shared document

The output format can change while the facts stay the same.

Destination	How to adapt the draft	Keep separate
Jira-style issue	Move the concise title to Summary; map environment and reproduction text into the team's fields.	A suggested severity and approved team priority.
GitHub issue	Use Markdown headings and the repository's existing template where available.	A draft issue body and an actually submitted issue.
Shared QA worksheet	Record one issue per entry with the case ID and evidence location.	The observed failure and a later rerun result.
Developer discussion	Start with the smallest clear observation and missing facts.	A suspected cause and a confirmed diagnosis.

Brainbaby can help draft wording from your notes. A dedicated issue tracker manages ownership, state and review history. A capture tool may collect a recording or environment data under its own permissions. Do not treat a text drafting workflow as equivalent to those tools' integrations or capture features.

Intermittent bugs, severity and missing evidence

What if the bug happens only sometimes?

Report the observed attempt, setup and sequence. If you later repeat the same procedure five times and see the failure twice, record 2 failures in 5 attempts for that exact setup. Do not generalize it to all users or call a different experiment the same reproduction. If you have not repeated it, frequency stays unmeasured.

Should AI choose severity and priority?

AI can suggest a provisional severity based on described impact, but missing context can change it. A blocked payment or data-loss observation needs different triage from an awkward label. Priority also depends on release timing, affected users and team policy. Leave final priority to the responsible team; do not invent an approval.

Can AI write a report from a screenshot alone?

It can help describe supplied visible content, but a screenshot usually does not reveal the full action sequence, build or repeat rate. Name those missing facts and supply only images you are authorized to share. The planner on this page accepts text; an evidence filename is not an uploaded screenshot.

What should happen after the report is drafted?

Review the facts, rerun proposed steps in an appropriate environment, then submit through your normal process. For implementation help, use Brainbaby's coding workflow with a minimal reproducible example and the access you intend to provide. Keep test execution and release approval separate from generated suggestions.

Start with one observation in the report planner. A shorter report with a reliable expected rule and an honest missing-evidence list gives your teammate a better starting point than a longer report full of invented certainty.