Gains:
- Ability to transform scattered observations into a report containing a clear title, deterministic reproduction steps, expected/actual results and evidence with the support of artificial intelligence
- Being able to impose the rule of 'only use the information I give, do not make it up' to artificial intelligence and guarantee reproducibility with its own control
- Being able to distinguish between severity (technical impact) and priority (business urgency) and give the final label with the business context
The bug a tester finds is only valuable if it is fixed; Fixing it depends largely on the quality of the bug report—a record that documents a defect in a way that the developer can understand, reproduce, and fix it. A poorly written bug report ("login not working") will stall the developer for hours, lead to back-and-forth correspondence, and often close as "cannot reproduce". A good report includes clear steps, expected and actual results, context information and evidence. Artificial intelligence (AI) is very good at turning your scattered observations into a professional, structured report. But the central caveat applies here too: AI cannot make up steps you don't see; can fill in the missing information with “reasonable-looking” but inaccurate guesses. Your job is to make sure that every line of the report is based on what you actually observed.
Anatomy of a good bug report
An effective report includes these components:
- Title: Short, specific, searchable. Not "There is an error"; "Unable to click 'Checkout' button with more than 10 items in cart (Chrome)".
- Steps to reproduce: Numbered, traceable from scratch, deterministic. The developer should be able to see the error after following these steps.
- Expected result: What should have happened according to the acceptance criteria.
- Actual result: What happened (error message, screen, behavior).
- Environment: Browser/device, version, environment (test/live), user role, data.
- Evidence: Screenshot, video, log, error trace (stack trace).
- Severity and priority: Detailed below.
Tip: Before sending a report, ask "if I give these steps to someone else, can they see the error without my help?" ask. If the answer is "no", the report is incomplete. AI can make the report beautiful, but only you can guarantee reproducibility.
Violence and priority: two confused concepts
Severity is the technical effect of the error: does the system crash, data is lost, or is it a typo? Priority is how urgently it needs to be fixed; is about business impact. The two don't always go in the same direction: misspelling the company name on the homepage is low severity but high priority (reputation). In a rare edge case, a collapse may be of high severity but low priority. AI helps you make this distinction when you give the observation; but the final label is given by you who know the business context.
violence
example
priority
example
Critical (Blocker)
Payment cannot be completed
Urgent (P1)
Loss of income in live
High (Major)
Report gives incorrect total
High (P2)
A must for upcoming release
Medium (Minor)
Rare edge case error
Medium (P3)
In a planned sprint
Low (Trivial)
Button alignment is off
Low (P4)
When there is a chance
Weak prompt / Strong prompt
Weak: "Report this error: payment is not working."
Strong: "Translate my observations below into standard bug report format: title, reproduction steps (numbered), expected result, actual result, environment, severity and priority recommendation (justified). Use only the information I provide; make up any missing fields, mark 'INFORMATION MISSING: ...'. Observations: Chrome 120, test environment, 12 items in cart, nothing happens when I press 'Checkout', 'undefined is not a function' error in the console, There is no problem with 11 products."
Powerful prompt; imposes the format, the "fitting" rule, and the marking of missing information. This way, the report will be both accurate and honest.
Duplicate error detection
In large teams, the same error is reported over and over again. AI can compare your new report to existing open bugs and flag potential duplicates — this keeps your bug tracking system (Jira, Azure DevOps, GitHub Issues) clean. But beware: two errors that appear similar on the surface may have different root causes; Compare the repeat production steps and environment of both reports before closing the AI's "duplicate" suggestion. An accidentally closed "duplicate" is actually missing a separate error.
From bug trace to root cause: The power of AI to read logs
The most technical part of a bug report is often the bug trace (stack trace — a breakdown of which line of code, with which call chain, triggered a bug). Long and complex logs can tire even the developer. The AI reads a log of hundreds of lines and summarizes in seconds the most critical lines, the possible root cause hypothesis, and the code point where the error was triggered. This both shortens the report and gives the developer a direct starting point.
Remember two limits, though. First, the root cause given by AI is a hypothesis, not evidence; The developer should not attempt to fix this without verifying it. Second, logs often contain personal data (email, user ID, session token); Mask these areas before placing the log on the vehicle. A good practice is to first have the AI say "list the fields that need to be masked in this log" and then analyze the cleaned log.
Tip: Instead of pasting the entire log into the report, include the most critical 3-5 lines that AI summarizes and a link to the full log. This way the report remains readable, and the developer who needs details can access the full log.
Four copyable templates
1) From observation to report:
Your role: senior QA. Translate the following raw observations into a standard bug report: Title / Reproduction steps (numbered) / Expected / Actual / Environment / Evidence note / Severity + Priority (justified). RULE: use only the information I provide; mark the missing field as "MISSING INFORMATION:..." Observations: [raw notes]
2) Reproducibility control:
Read this bug report from the perspective of a developer who has never seen the bug. Follow the steps and mark the places where it won't produce the bug: ambiguous step, missing prerequisite, missing test data, skipped condition. Tell me what information I should add for each gap. Report: [paste report]
3) Severity/priority advisor:
I describe the following error: [error + business context]. Give suggestions and justification separately for severity (technical impact) and priority (business urgency). Explain why the two might be different. I will make the final decision.
4) Log/error trace summary:
Examine the error trace/log below. Give me a summary of (1) the root cause hypothesis, (2) the likely code point where the error occurred, (3) the 3 most critical lines to add to the report. Mask if there is personal data.Log: [paste log]
three mini cases
Case 1 — Liberation from “I Couldn't Produce.” In one team, 30% of bugs were closed as "cannot reproduce". The "reproducibility check" template has been added to the report process; Before each report was sent, the AI flagged missing steps and prerequisites. Three months later, the "couldn't produce" rate dropped from 30% to 8%. The difference was that the steps were exact from the beginning.
Case 2 — The danger of fake steps. A tester had the AI write a report with incomplete observations; AI added a step that never happened, such as "user turns on notifications from the settings page". When the developer followed that step, he could not find the error and lost time. The team enforced a "only use the information I give, don't make it up" rule; Made-up steps are eliminated.
Case 3 — Severity/priority distinction. There was a typo in the company slogan on the home page. The tester would pass this off as "low"; The AI consultant reminded that technical violence is low but business priority is high (the reputation element that every visitor receives). The bug was fixed the same day with the "high priority" tag.
Common mistakes
- Vague title. Unsearchable, non-discriminating headlines like "Not working".
- Missing/skipped steps. Not writing what is obvious in your context; developer's failure to produce.
- Letting the AI make it up. Having the missing information filled in with a "reasonable estimate"; wrong steps.
- Not writing the expected result. Saying "wrong" but not specifying what is right.
- Confusing violence and priority. Mistaking the two as one label; Misjudging business impact.
- Sensitive data in evidence. Sharing real personal data in screenshots/logs without masking them.
In summary
The value of the bug report is that the developer can reproduce and fix the bug without your help. AI is very good at turning scattered observations into a professional, structured report; It organizes the title, steps, expected/actual result, environment and evidence, and provides consultancy on the distinction between severity and priority. But AI can make up for missing information; Enforce the "only use the information I give, mark the missing" rule and guarantee reproducibility yourself. Mask personal data in evidence.
Application task
Take a bug you've recently found and turn your raw observations into a report using the "observation to report" pattern (with the "fitting" rule). Then perform the “reproducibility check” and fill in the marked gaps. Give the report to a colleague and see if he can produce the error without your help. Finally, determine the labels with the "violence/priority consultant" and finalize it at your own discretion. Take note of any information the AI tries to make up in the process.
checklist
- [ ] My title is specific and searchable.
- [ ] Reproduction steps are from scratch, deterministic and complete.
- [ ] I wrote the expected and actual results separately.
- [ ] The setting and evidence information is complete; I masked personal data.
- [ ] I imposed the "make it up, mark the missing" rule on the AI and filled in the gaps myself.
- [ ] I evaluated the severity and priority separately and made the final decision.