Use AI as an additional reviewer—not as the authority on what a legacy system is supposed to do. First establish which builds, tests and static-analysis checks pass today; then give the reviewer trustworthy project context, verify its findings against intended behavior, and keep accountable humans and pull-request protections in control of merging.
How do I use AI code review on a legacy codebase?
Start by treating the review as one stage in your existing change-control process. Older systems often have sparse tests, undocumented behavior and subsystem-specific conventions; a reviewer may see the code but not know which apparent oddities are deliberate. GitHub Docs says thorough review is especially critical for legacy codebases and larger pull requests. That is workflow guidance, not evidence that a particular AI reviewer reduces defects or raises productivity in legacy repositories.
1. Establish a baseline before the review
Run the checks the project can actually run before asking AI to assess the change. GitHub Docs puts it plainly: “Always run automated tests and static analysis tools first.” Record their results, including existing failures and warnings. A failing check that was present before the change is different from a new regression; a passing check is not proof that the change preserves behavior.
- Build or compile the relevant target, if the project has a reproducible build.
- Run the available test suite, noting which tests cover the changed behavior and which do not.
- Run configured static analysis and record existing as well as new findings.
- If coverage is thin, identify the most relevant checks that are feasible and make the gap explicit in the review.
Ask the reviewer to identify missing functional tests or edge cases when useful, but treat those suggestions as hypotheses. Confirm any proposed test against the real system behavior before relying on it as a regression check.
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2. Give the reviewer local, authoritative context
Provide the README, relevant design notes, architecture information, recent pull requests and the conventions that apply to the changed subsystem. Make clear which sources are authoritative, which examples are outdated, what compatibility constraints matter, and which unusual behaviors are intentional. Recent changes can help show how the team handles similar code, but they should not override explicit project rules.
For GitHub Copilot, the documented instruction scopes include .github/copilot-instructions.md for repository-wide guidance, matching *.instructions.md files under .github/instructions/ for path-specific guidance, and AGENTS.md for context usable across tools. Skills can provide task-specific workflows. Use narrower instructions where legacy subsystems genuinely differ, and keep the guidance aligned with the head branch being reviewed. Copilot code review can also use repository-level skills and configured MCP servers for relevant internal context, such as issues, documentation, service catalogs or incident tooling.
A practical review request can be explicit without pretending to know the answer in advance:
Review this change against the documented behavior and conventions for the affected subsystem.
Prioritize compatibility regressions, edge cases, security risks, and deleted or skipped tests.
For each finding, identify the changed code and explain the failure scenario and assumptions.
Do not treat existing failures as regressions. If behavior or a convention is unclear, say what
information is missing rather than assuming a new rule.Keep shared rules in shared guidance and subsystem exceptions near the paths they govern. That reduces the chance that a broad instruction designed for one part of an old application will be applied incorrectly to another.
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3. Check behavior and assumptions, not just style
For each finding, inspect the cited change, its call path and the assumptions behind the warning. Ask whether the code solves the requested problem, respects the architecture, preserves existing behavior, handles relevant edge cases and remains maintainable. A plausible comment is not established fact: reject or investigate it if it conflicts with confirmed business behavior or cannot be reproduced.
Verify unfamiliar APIs rather than accepting their names at face value. Inspect every new dependency for existence, maintenance status, provenance and license compatibility. GitHub’s guidance warns that AI-generated reviews can hallucinate APIs, miss constraints, get logic wrong, overlook deleted or skipped tests, and suggest suspicious or nonexistent packages. Its examples of prompts to look for missing tests or possible vulnerabilities are useful starting points, not a guarantee that the reviewer will find every issue.
How do I keep AI code review from breaking existing behavior?
Make compatibility an explicit review question. In a poorly documented system, the key issue is often not whether a change looks cleaner, but whether it preserves behavior that callers, users, data formats or operations depend on. Record known invariants in project guidance, identify the relevant code paths, and compare the proposed change with tests and confirmed behavior. Where intent remains uncertain, ask the responsible maintainer or domain owner before accepting a rewrite.
- Check edge cases and error handling on the affected paths, including behavior relied on by callers.
- Review test changes as carefully as implementation changes; a removed or skipped test can hide a regression.
- Distinguish an established project pattern from a genuinely safe change. A reviewer may miss either an important exception or a reason to update an old convention.
- For a suggested fix, verify that it addresses the reported scenario without changing unrelated behavior.
When coverage is missing, use a suggested test as a prompt for investigation, not as proof of intended behavior. A test that encodes an incorrect assumption can make a regression harder to see.
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What checks should run alongside AI review?
Keep deterministic checks in the workflow because they answer different questions from a language model’s review. Run the tests and static analysis relevant to the change, and inspect the difference between the baseline and the new results. Security and dependency checks can add further signals, but no single tool covers every defect class.
- Tests and build: show whether defined examples and build targets still pass, subject to their coverage.
- Static analysis: can flag configured classes of issues independently of the reviewer’s reasoning.
- Security and dependency tooling: GitHub’s examples include CodeQL for vulnerability checks and Dependabot for vulnerability and dependency issues.
- Reliability and maintainability signals: GitHub Code Quality is another example, with a different purpose from tests or dependency scanning.
Compare findings with the recorded baseline so pre-existing problems are not mislabeled as change regressions. Conversely, do not dismiss a new warning merely because the project already has other warnings.
Should AI code review approve a pull request?
Not on its own. Use a model’s assessment as a review signal, not as authorization to merge. Have a teammate review complex or sensitive changes, with attention to functionality, security and maintainability, and require approved pull requests for production or other important branches.
GitHub documents that Copilot’s approval assessment does not count toward merge requirements by default. Its approval behavior is configurable, and the documentation describes Copilot approvals as public preview. Do not treat an assessment as an independent approval policy; confirm the repository’s actual branch protections and approval configuration.
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GitHub’s rollout guidance says: “Developers and bad actors should never be able to unilaterally apply unvetted AI suggestions or agent work directly to sensitive codebases.” The operational point is that review and merge authority should remain governed by your team’s controls, especially for sensitive systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I choose review depth and budget?
GitHub describes two Copilot review effort levels. The documented figures below are vendor estimates in USD per review, recorded in GitHub Docs as accessed in 2026—not guaranteed prices. Actual consumption generally rises with pull-request size and repository instructions, can change as models evolve, and does not include GitHub Actions minutes.
| Copilot effort | GitHub’s description | Estimated usage per review | Best fit in GitHub’s guidance |
|---|---|---|---|
| Lite | Cost-efficient, targeted review of common issues | $0.05–$1 USD; GitHub Docs estimate, accessed 2026 | Routine changes where speed matters more |
| Balanced | Deeper analysis using a higher-reasoning model | $0.25–$5 USD; GitHub Docs estimate, accessed 2026 | Complex logic, security-sensitive work or changes spanning services |
GitHub describes two usage components: AI credits for model interaction and Actions minutes for agentic context gathering and tool use. Its guidance recommends Balanced for security-sensitive or multi-service pull requests and Lite for routine work where speed is more important. Check current rates, entitlements and billing configuration before forecasting spend. Copilot’s agentic capabilities can use GitHub-hosted or self-hosted Actions runners; self-hosted runners do not consume Actions minutes, while larger GitHub-hosted runners have higher per-minute billing.
Also check what automatic review excludes before relying on it for coverage. GitHub documents exclusions including dependency-management files such as package.json and Gemfile.lock, log files and SVG files. Route those changes to suitable human, dependency or static-analysis checks rather than assuming the AI review covered them.
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How should I compare AI code review tools?
Compare how a tool fits the repository and its controls, not just whether it can comment on a pull request. The available product documentation here supports several comparison questions for Copilot, but does not establish a like-for-like independent ranking across vendors.
| Comparison area | Questions to ask |
|---|---|
| Repository context | Can it use project documentation, shared and path-specific rules, and relevant issue or incident context? |
| Change and review depth | Does it review the diff, gather broader repository context, and offer a review depth suited to the change’s risk? |
| Validation coverage | Which tests, static analysis, security checks and dependency tools remain necessary, and what integrations are available? |
| Exclusions | Which file types or change patterns are not reviewed, and who checks them instead? |
| Governance | Can human approvals, branch protections, audit requirements and incident processes remain authoritative? |
| Cost | What is billed for model use, context-gathering actions and users without included entitlements? How does usage change with change size and configuration? |
| Privacy and deployment | What do the organization’s current plan terms say about data use, retention, region and runner or deployment guarantees? |
Privacy, retention, regional handling and deployment guarantees depend on current vendor terms and the organization’s plan; verify them with the vendor and procurement team rather than assuming they are settled by a feature description.
What is a useful legacy-code reference?
Michael Feathers’s Working Effectively with Legacy Code is a practical reference for making changes in large, untested codebases and writing tests that protect against unintended changes. Pearson lists the first edition as a print text, ISBN 9780131177055. It is relevant to safe change practices, not a manual for AI code review.
Source notes for product-specific details: GitHub Docs, “Review AI-generated code”; “Maintaining codebase standards in a GitHub Copilot rollout”; and the Copilot review effort and usage documentation, accessed October 4, 2026. Product configuration, estimates, entitlements, previews and exclusions can change; confirm the current documentation and settings for your organization.
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