Debug and fix open-source bugs with tests via agents
I've submitted two PRs to fix bugs in open-source system tool last month. Task: I've submitted two PRs to fix bugs in open-source system tool last month. Both of these are targeted fixes to highly-specific bugs that existed in this software. The agents I used debugged the issue and implemented fixes and tests autonomously.These problems looked slightly more difficult than bubble sort to me. Do you suppose these bugs existed in some other software and the agent only translated the fix between repositories? When the agent was debugging the software, were they simply following some tutorial they were trained on? Is this the 1% case? Or are the stochastic parrots regurgitating most probable tokens in approximately the right order to provide an illusion of work being done?> In case you would say "but LLM-s learn on random source code from the internet"Of course they don't, it's not 2022 any more.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: The source does not specify the agent's architecture, the human approval workflow, or the final status of the PRs. It is unclear if the fixes were accepted or if they introduced regressions. Reliance on autonomous agents for debugging carries risks of hallucinated solutions or superficial fixes that pass tests but fail in production..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
I've submitted two PRs to fix bugs in open-source system tool last month.
Workflow
I've submitted two PRs to fix bugs in open-source system tool last month. Both of these are targeted fixes to highly-specific bugs that existed in this software. The agents I used debugged the issue and implemented fixes and tests autonomously.These problems looked slightly more difficult than bubble sort to me. Do you suppose these bugs existed in some other software and the agent only translated the fix between repositories? When the agent was debugging the software, were they simply following some tutorial they were trained on? Is this the 1% case? Or are the stochastic parrots regurgitating most probable tokens in approximately the right order to provide an illusion of work being done?> In case you would say "but LLM-s learn on random source code from the internet"Of course they don't, it's not 2022 any more.
Reported result
Two PRs were successfully submitted to the open-source project. The author questions whether the agents truly solved novel problems or merely regurgitated training data patterns. No metrics on acceptance rate or code quality are provided, leaving the actual business impact and reliability of the fixes unknown.
Human role
The original post does not say how a person stayed involved.
Limitations
The source does not specify the agent's architecture, the human approval workflow, or the final status of the PRs. It is unclear if the fixes were accepted or if they introduced regressions. Reliance on autonomous agents for debugging carries risks of hallucinated solutions or superficial fixes that pass tests but fail in production.
Try it
Act as a senior software engineer using an AI coding agent to fix a specific bug in [replaceable: open-source repository URL]. First, identify a reproducible bug from the issue tracker. Instruct the agent to analyze the code, propose a fix, and write unit tests. Run the tests locally to verify the fix. Present the diff and test results to me for review. Do not push changes or create a PR until I explicitly approve the code. If the agent fails to reproduce the bug or the tests fail, stop and report the error. Ensure the fix is minimal and does not include unrelated refactoring.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.