Elevating Bug Writing with AI: A Look at Claude Code's New Skill for Generating Bug Reports from Real Evidence
An in-depth look at part 8 of the Automating Playwright series on building a Claude Code skill that turns discovered issues into ready-to-send bug reports, complete with guardrails to prevent hallucinations.
Stock photo for illustration only, not from the actual event
- This is part 8 of the Automating Playwright with Claude Code series, focusing on building a bug-reporting skill.
- It takes the problems detected in part 6 and converts them into bug reports with step-by-step reproduction based on real evidence.
- It uses the Guardrails pattern from part 7 to prevent data fabrication and force human review before actual submission.
- Reports consist of title, steps, expected results, actual results, evidence, and severity level.
We have reached part 8 of the "Automating Playwright with Claude Code" series. This article builds upon part 6—where our system could already detect issues like occasional test failures or locator problems—and part 7, which covered safety guardrails, combining them to create a new skill named Bug-Reporter.
Previously, Claude's skills usually ended just with a notification that something went wrong. This article bridges that gap by creating a skill that transforms the failure Claude just discovered into a production-ready bug report, featuring reproduction steps referenced from evidence rather than guesswork.
Stock photo for illustration purposes only, not from the actual event.
Before writing the code for this skill, it is necessary to define the data fields that a good bug report should have, which will become the skill's output template: title, reproduction steps, expected results, actual results, evidence, and environment. At the same time, the guardrails rules from part 7 are immediately applied, since fabricated bug reports would cause significant damage and waste time for the development team.
Using AI to help report bugs automatically carries the risk of "hallucination," where the AI might try to fill in missing information gaps to make the report look complete. This article's emphasis on guardrails—prohibiting steps that were not actually performed and preventing automatic submission to bug-tracking tools—serves as a safe practice that helps maintain the credibility of the software development process.
The ironclad guardrails rules in this system include:
- Prohibiting any reproduction steps that were not actually performed and observed firsthand during that session.
- If evidence for any field is missing, such as no logs or screenshots, explicitly stating that it is missing rather than leaving it blank or guessing.
- Prohibiting automatic ticket submission to tracking systems; reports must always be presented to users for confirmation to prevent duplicate tickets.
As a working example, when testing a checkout page where an issue is found where the "Place Order" button can be clicked immediately before the cart total finishes updating, the playwright-flaky-test-debugger system from part 6 runs first to collect trace evidence. Then, the playwright-bug-reporter generates a report in the specified format, noting that the button remains clickable during recalculation and featuring trace evidence showing the element has an aria-busy="true" value at timestamp 00:04.211.
"Checkout — "Place Order" clickable before cart total finishes updating"
Example bug report title from the article
The template created here is designed to be neutral and unbound to any specific bug-tracking system, allowing teams to apply it to Jira, GitHub Issues, or other tools as needed. By closing the loop from issue discovery to value generation, the toolsets from parts 6 and 8 work together seamlessly across detection, diagnosis, and verifiable report writing.
Source: Dev.to
Found something wrong in this article? Report an issue with this article
Comments
Leave a Comment