Who should use the Debug code workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
A concise workflow to identify and fix bugs in code using AI-powered debugging and explanation tools.
Deliverable outcome
The fix is documented for maintainers, reducing the chance of reintroducing the same bug.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
The fix is documented for maintainers, reducing the chance of reintroducing 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 Kilo Code v7 to a reproducible minimal test case and a clear location (file, function, line) where the bug occurs. Then, you pass the output to Gemini 2.5 Pro to a clear, ai-generated explanation of the root cause (e.g., off-by-one error, null reference, race condition). Then, you pass the output to Qodo to one or more validated fix candidates with an understanding of their implications. Then, you pass the output to Claude Code to the bug is fixed, and all existing tests pass without new failures. Finally, Korbit is used to the fix is documented for maintainers, reducing the chance of reintroducing the same bug.
Reproduce and Isolate the Bug
A reproducible minimal test case and a clear location (file, function, line) where the bug occurs.
Analyze Root Cause with AI
A clear, AI-generated explanation of the root cause (e.g., off-by-one error, null reference, race condition).
Generate and Review Fix Suggestions
One or more validated fix candidates with an understanding of their implications.
Apply Fix and Verify
The bug is fixed, and all existing tests pass without new failures.
Document the Bug and Fix (optional)
The fix is documented for maintainers, reducing the chance of reintroducing the same bug.
Run the code with the exact input or scenario that triggers the bug. Use minimal test cases to narrow down the location of the issue, such as a specific function, module, or line number. Document the observed behavior versus expected behavior.
Why Kilo Code v7: Kilo Code v7 explicitly includes debugging errors and tracing root causes, which directly supports reproducing and isolating bugs in a code editor environment.
Paste the isolated code snippet, error message, and expected behavior into an AI debugging tool (e.g., ChatGPT, GitHub Copilot Chat, or an LLM-powered IDE plugin). Ask the AI to explain the root cause, including any logical errors, type mismatches, or edge cases. Review the explanation critically.
Why Gemini 2.5 Pro: Gemini 2.5 Pro is designed for complex multi-step reasoning and code debugging, making it ideal for analyzing root causes of bugs.
Ask the AI to propose one or more fixes for the identified root cause. Review each suggestion for correctness, side effects, and alignment with the codebase style. If multiple options exist, compare trade-offs (e.g., performance vs readability).
Why Qodo: Qodo specializes in code review, bug detection, and compliance checks, directly matching the need for generating and reviewing fix suggestions.
Implement the chosen fix in the codebase. Run the original failing test case to confirm the bug is resolved. Execute a broader test suite (unit, integration) to ensure no regressions were introduced.
Why Claude Code: Claude Code supports automated bug fixing and test generation, directly enabling applying fixes and verifying them with tests.
If the bug is subtle or likely to recur, add a comment in the code explaining why the fix works and what caused the original issue. Optionally, update project documentation or a bug tracker with the root cause and resolution steps for future reference.
Why Korbit: Korbit generates PR descriptions and bug detection summaries, directly supporting documentation of bugs and fixes.
§ Before you start
Teams or solo builders working on development 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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