How to Automate Bug Fixes on GitHub

Manual bug fixing is slow and repetitive. A developer reads an issue, digs through the codebase, writes a patch, adds tests, and opens a pull request. For straightforward bugs, this process can eat up hours of focused engineering time. Modern AI tools can now automate the entire flow from issue to PR, letting your team focus on the work that actually requires human judgment.

The Manual Bug Fix Workflow

Every engineering team knows this loop. A user or teammate files a GitHub issue. A developer picks it up, reads the description, and starts investigating. They search through the codebase for the relevant files. They reproduce the bug locally. They form a hypothesis, write the fix, then write a test to prove it works. Finally, they open a pull request, write a description, and wait for review.

For a simple null pointer guard or a missing validation, that entire cycle takes 30 minutes on a good day. For a bug that spans multiple files or requires understanding a subsystem, it can stretch to 2-4 hours. Multiply that across dozens of bugs per sprint, and you are looking at a significant chunk of your team's velocity consumed by routine fixes.

The real cost is not just the time spent fixing. It is the context switching. A developer deep in a feature gets pulled away to triage a bug, loses their train of thought, and takes 20 minutes to get back into flow. Studies estimate that context switches cost teams 20-40% of their productive time.

What Automated Bug Fixing Looks Like

With an automated bug-fixing tool, the workflow compresses dramatically. Here is what it looks like end to end:

1. A teammate files an issue describing the bug.
2. Someone adds a label (e.g., plip) to trigger the automation.
3. The AI agent reads the issue, clones the repository, and analyzes the codebase.
4. It forms a hypothesis about the root cause, writes a fix, and adds a regression test.
5. It runs your full test suite to verify nothing is broken.
6. It opens a pull request with a clear explanation of the changes.
7. A human reviews the PR and merges it.

Total time from label to PR: 3-10 minutes. The developer who would have spent an hour on this never leaves their current task. They just review a ready-made PR when they have a moment, and move on.

Setting Up Plip (Step by Step)

Plip is a GitHub App that automates this exact flow. Setup takes about two minutes and does not require any configuration files or CI changes.

Title: Login form accepts empty email field

When I click "Sign In" with an empty email field, the form
submits and returns a 500 error from the API.

Expected: The form should validate that the email field is
not empty before submitting.

Steps to reproduce:
1. Go to /login
2. Leave the email field blank
3. Enter any password
4. Click "Sign In"

Error: 500 Internal Server Error from POST /api/auth/login

Writing Good Bug Reports for AI

The quality of the fix depends heavily on the quality of the issue. AI agents work best when they have clear, structured information. Here is what to include in every bug report you want Plip to handle:

• Error messages: Paste the exact error output. Stack traces are especially helpful because they point directly to the file and line where things went wrong.
• Expected vs. actual behavior: Spell out what should happen and what is happening instead. Do not assume the reader knows what "correct" looks like.
• Steps to reproduce: A numbered list of steps to trigger the bug. The more concrete, the better. Include specific inputs, URLs, or API endpoints.
• Relevant file paths: If you already know which files are involved, mention them. This saves the AI significant search time and improves accuracy.

Here is a minimal template you can use:

## Bug description
[What is broken]

## Expected behavior
[What should happen]

## Actual behavior
[What happens instead]

## Steps to reproduce
1. ...
2. ...
3. ...

## Error output
```
[Paste error/stack trace here]
```

## Relevant files (optional)
- src/auth/login.ts
- src/components/LoginForm.tsx

A well-written issue does not just help AI. It helps every developer on your team. The discipline of writing clear bug reports pays dividends whether or not you are using automated tools.

What Plip Does Under the Hood

When you add the plip label to an issue, here is what happens behind the scenes:

1. Clone and sandbox: Plip clones your repository into an isolated sandbox environment. Your code never leaves a secure, ephemeral container.
2. Codebase analysis: The agent reads your project structure, dependency files, test configuration, and the specific files related to the bug. It builds a mental model of how your code is organized.
3. Hypothesis formation: Based on the issue description and codebase context, the agent forms a hypothesis about the root cause. It traces the execution path from the reproduction steps to the failure point.
4. Fix implementation: The agent writes the minimal code change needed to address the bug. It follows your existing code style and patterns.
5. Regression test: Plip writes a test that would have caught this bug. The test verifies both the fix and the original failure condition, ensuring the bug cannot silently reappear.
6. Test suite execution: The full test suite runs inside the sandbox. If any test fails, the agent iterates on the fix until all tests pass.
7. Pull request: Plip opens a PR against your default branch with a detailed description of the root cause, the fix, and the test it added. The PR links back to the original issue.

The entire process is agentic. If the first attempt does not pass tests, Plip reads the failure output, adjusts its approach, and tries again. This loop continues until the fix is verified or the agent determines it cannot resolve the issue with high confidence.

When to Use Automated Fixes vs. Manual

Automated bug fixing is not a replacement for engineering judgment. It is a tool that handles the routine work so your team can focus on harder problems. Here is a practical breakdown of where it excels and where you should stick with manual fixes.

A good rule of thumb: if a senior developer would describe the fix as "straightforward" after reading the issue, it is a strong candidate for automation. If the fix requires debate, design discussion, or cross-team coordination, keep it manual.

Getting Started

Plip's free tier includes 10 fixes per month, which is enough to evaluate it on real bugs in your codebase. There is no credit card required and no configuration files to set up. Install the GitHub App, label an issue, and see the result in minutes.

Have questions about automated bug fixing or want to see how Plip handles a specific type of issue? Open an issue on our public repo and we will help you get started.