Use an AI coding assistant to generate hypotheses and propose a bounded fix—not to decide that a bug is solved. A reliable workflow gives it concrete failure evidence and trusted project context, lets a developer inspect the proposed diff, verifies the change independently, and requires human approval before integration.
1. Capture the failure before asking for a fix
Start with what you can observe, not a guess about the cause. Record the expected behavior, actual behavior, reproduction steps, and the relevant error details. For an exception, include its message, type, stack trace, and the source line where it is thrown; these details help make a debugging request specific rather than speculative. Microsoft Research’s 2024 paper on AI-assisted code debugging describes this kind of exception context.
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- Expected: What should happen?
- Observed: What actually happened, and how does it differ?
- Reproduction: What steps, inputs, or conditions reliably trigger it?
- Evidence: What error, stack trace, logs, or source location point to the failure?
If the problem is intermittent or you cannot reproduce it, say so. A clear statement of what is unknown is more useful than presenting an uncertain explanation as fact.
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Provide only the repository context relevant to the failure: the affected code, related tests, project conventions, and constraints on behavior. State which files or project instructions are authoritative, and identify behavior that must not change. GitHub recommends grounding AI code review in trusted project context and requirements rather than treating generated suggestions as self-validating. See GitHub’s guidance on reviewing AI-generated code.
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Be explicit about scope. For example: “Diagnose why this input causes the reported exception. Do not change the public API or alter behavior for other inputs. First list likely causes and the evidence for each; then propose the smallest change and a test.” This asks for analysis before edits and makes assumptions easier to challenge.
3. Ask for a diagnosis before broad edits
Request a short, evidence-based diagnosis before asking for a patch. Ask the assistant to distinguish likely causes from possibilities, point to the code or failure evidence supporting each, identify missing information, and propose the smallest change that could address the defect. If it cannot explain how a change follows from the observed failure, do not accept the patch merely because it looks plausible.
Keep the proposed change small enough to review. A tightly scoped patch is easier to compare with the bug report, test, and project architecture; a broad rewrite makes it harder to tell whether the defect was actually addressed or unrelated behavior shifted.
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Read the changed files yourself before running or merging the patch. Check whether it fixes the reported failure, meets the expected behavior, follows the project’s architecture and conventions, and avoids unrelated changes. GitHub’s review guidance also highlights risks particular to AI-generated changes, including invented APIs and tests that have been removed or skipped. Use its checklist as a prompt for functional, security, and maintainability review.
- Are referenced APIs and dependencies real and appropriate for this project?
- Does the change introduce dependency, licensing, or security concerns?
- Were existing tests deleted, weakened, or bypassed?
- Does the patch preserve behavior outside the stated scope?
- Can you explain why each changed line is needed?
Repository-level and path-specific instructions, along with security checklists, can help make reviews more relevant to the codebase. GitHub documents these approaches for Copilot code review. Read about using GitHub Copilot code review.
5. Verify independently
Run checks that test the change rather than relying on the assistant’s explanation. Start with the relevant build or compile step and targeted tests, then run regression tests and appropriate static-analysis or security checks. Inspect warnings and failures; do not describe a check as passed if it was not run.
A green test suite is evidence, not proof that the patch matches the intended behavior, fits the architecture, or is safe. Automated checks cannot replace a developer’s review of the change’s purpose and consequences. GitHub’s guidance recommends combining functional checks with scrutiny of project intent, architecture, dependencies, and security. Review AI-generated code with those dimensions in mind.
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A developer should accept, edit, or reject the patch after reviewing the diff and validation results. Require human approval before merging or allowing an agent to take consequential actions. NIST’s DevSecOps guidance calls for monitoring and human validation of AI-generated content, as well as governance, authorization, auditability, and human oversight for agent actions. See NIST NCCoE’s DevSecOps documentation introduction.
When the project needs an audit trail, record the prompt or context summary, proposed and accepted diffs, checks and their results, reviewer decision, and unresolved risks in the pull request or issue. Keep the record factual: distinguish what passed, what failed, and what remains untested.
Quick Recap
A practical review checklist
- Does the patch reproduce and fix the reported failure?
- Does it satisfy the stated expected behavior without unrelated changes?
- Are its APIs and dependencies real, appropriate, maintained, and compatible with the project’s licensing needs?
- Were meaningful tests added or updated without deleting or bypassing existing coverage?
- Were compilation, tests, static analysis, and security checks run as appropriate—and are their results recorded accurately?
- Did a human inspect and approve the actual diff before integration?
- Is the record clear about failures, untested areas, and unresolved risk?
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