Who should use the Code Debugging workflow?
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Work
Practical execution plan for code debugging with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A permanent record of the fix and a new test that guards against the same bug.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A permanent record of the fix and a new test that guards against the same bug.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Devin to a clear, repeatable reproduction of the bug and a narrowed-down location in the codebase. Then, you pass the output to Devin to a precise description of the discrepancy (e.g., 'variable x is null, but should be a string'). Then, you pass the output to Factory to a confirmed root cause (or elimination of false leads) with a minimal code change that resolves the bug. Then, you pass the output to Devin to a committed, tested code change that eliminates the bug without introducing new issues. Finally, GitHub Copilot is used to a permanent record of the fix and a new test that guards against the same bug.
Reproduce and Isolate the Bug
A clear, repeatable reproduction of the bug and a narrowed-down location in the codebase.
Understand the Expected vs Actual Behavior
A precise description of the discrepancy (e.g., 'variable X is null, but should be a string').
Formulate and Test Hypotheses
A confirmed root cause (or elimination of false leads) with a minimal code change that resolves the bug.
Apply the Permanent Fix
A committed, tested code change that eliminates the bug without introducing new issues.
Document and Prevent Recurrence
A permanent record of the fix and a new test that guards against the same bug.
Run the code in a controlled environment to trigger the bug consistently. Use minimal input or test cases to narrow down the exact conditions causing the failure. This step ensures you are debugging a real, reproducible issue, not a phantom error.
Why Devin: Devin is specifically designed for bug fixing and debugging, with autonomous capabilities to reproduce and isolate bugs across environments.
Compare the actual output or state against the documented or intended behavior. Write down what the code should do at the failing point versus what it does. This step transforms a vague error into a specific logical or data mismatch.
Why Devin: Devin's bug fixing and debugging capability includes understanding expected vs actual behavior through autonomous analysis.
Based on the discrepancy, propose one or two root causes (e.g., off-by-one error, missing null check, incorrect API endpoint). Modify the code temporarily to test each hypothesis, using assertions or unit tests to confirm. This step avoids random guessing and keeps debugging systematic.
Why Factory: Factory explicitly includes automated unit and integration testing, which is essential for formulating and testing hypotheses.
Write the corrected code, ensuring it handles edge cases and follows best practices. Run the full test suite to verify no regressions occur. This step delivers a robust solution, not just a patch.
Why Devin: Devin's end-to-end bug fixing and code refactoring capabilities ensure a permanent fix is applied and integrated.
Write a brief note in the codebase or issue tracker explaining the root cause and fix. Add a unit test that specifically covers the bug scenario to prevent it from reappearing. This step closes the loop and improves code quality for the future.
Why GitHub Copilot: GitHub Copilot's code explanation and documentation feature helps document the fix and prevent recurrence through better code understanding.
§ Before you start
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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