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Use an AI assistant to generate debugging hypotheses and candidate patches—not to make the final call. A reliable workflow gives it concrete failure evidence and trusted project context, asks for a bounded diagnosis, then has a developer inspect the diff, run appropriate checks, and explicitly approve, revise, or reject the change.
1. Capture the failure before asking for a fix
Start with what the program actually did and what it should have done. A precise report lets the assistant reason from evidence rather than guess at the problem.
- Observed and expected behavior
- Steps to reproduce the failure, including relevant inputs or environment details
- The exact error message and exception type, if available
- The stack trace and the source location where the exception is thrown
- Relevant logs, test output, or a minimal example that demonstrates the issue
Exception context such as the message, type, stack trace, and throwing line is also described in Microsoft Research’s 2024 paper on AI-assisted debugging: AI-assisted Code Debugging.
2. Give the assistant relevant, trusted context
Provide only the repository material needed to understand the failure: the affected code, related tests, relevant interfaces, and applicable project conventions. Identify which files or documentation are authoritative, state constraints, and say what behavior must not change. GitHub recommends grounding AI code review in trusted project context and requirements: Review AI-generated code.
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For recurring team reviews, repository-wide or path-specific instructions and security checklists can make the review more relevant to the codebase. See GitHub’s guidance on using GitHub Copilot code review.
3. Ask for a diagnosis before a broad edit
Keep the request narrow enough that a person can evaluate the answer. Ask the assistant to:
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- List plausible causes and the evidence for and against each
- Identify assumptions and missing information that could affect the diagnosis
- Suggest the smallest change that addresses the reproduced failure
- Explain which tests should demonstrate the fix and protect existing behavior
When the cause is unclear, resolve missing reproduction details before authorizing a sweeping rewrite. Treat the explanation as a hypothesis, not proof: a plausible account can still be wrong, and AI-generated code may rely on nonexistent APIs or misunderstand project intent.
4. Inspect the proposed diff
Review the actual changes, not just the assistant’s summary. Check whether the patch fixes the reported defect, fits the project’s architecture and conventions, and avoids unrelated behavior changes. Pay particular attention to:
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- Unexpected API or dependency changes, including whether a dependency is appropriate, maintained, and compatible with the project’s licensing requirements
- Tests that were deleted, weakened, skipped, or changed to avoid exposing the failure
- Security implications, input handling, and access to sensitive data or operations
- Changes that appear broader than the diagnosis requires
GitHub’s guidance recommends assessing functionality, project intent and architecture, dependencies, security, and maintainability when reviewing AI-generated code: Review AI-generated code.
5. Verify the patch independently
Run checks appropriate to the change; do not treat an assistant’s claim that a check passed as a result. A practical sequence is:
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- Reproduce the original failure so there is a baseline.
- Run the targeted test or reproduction against the patch and confirm the expected behavior.
- Run relevant regression tests and compile or build the affected program.
- Review warnings and run static-analysis and security checks where available and appropriate.
Passing checks are evidence that the patch works under the conditions tested. They do not establish that it matches the intended behavior, fits the architecture, or handles untested cases correctly. GitHub likewise recommends functional checks and review of intent, architecture, dependencies, and AI-specific risks such as hallucinated APIs or removed tests: Review AI-generated code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Make human approval the integration gate
A developer should decide whether to accept, edit, or reject the proposed patch after reviewing the diff and validation results. Require human approval before merging or before an agent takes consequential actions. NIST’s DevSecOps practices documentation emphasizes governance, authorization, auditability, monitoring, human validation, and oversight for AI actions: Introduction — Secure Software Development, Security, and Operations (DevSecOps) Practices documentation.
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7. Keep a traceable record when it matters
For changes that need review history or auditability, record the prompt and context summary, the proposed and accepted diff, checks actually run and their results, the reviewer’s decision, and any unresolved risk in the pull request or issue. Distinguish clearly between checks that passed, failed, or were not run; never imply validation that did not happen.
Human review checklist
- Does the patch reproduce and fix the reported failure?
- Does it meet the stated expected behavior without unrelated changes?
- Are APIs and dependencies real, suitable, maintained, and license-compatible?
- Were meaningful tests added without deleting or bypassing existing coverage?
- Were compilation, tests, static analysis, and security checks run where appropriate?
- Did a human inspect and approve the actual diff before integration?
- Does the record state what passed, failed, and remains untested?
How to assess a workflow or tool
Evaluate an AI-assisted debugging setup by how well it fits the team’s existing development and review process, uses relevant repository context, constrains and exposes proposed changes, supports testing and analysis, enforces human approval, and preserves an adequate decision trail. These are useful comparison criteria, not a basis for ranking vendors; the cited guidance does not establish a winner or a measured effectiveness percentage. For product-specific context, GitHub describes its coding assistant at GitHub Copilot.
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