Clean up AI-generated code with a staged, independent pipeline: format and lint first, measure complexity and duplication, run tests that the generating agent did not control, scan code and dependencies, then require a human-owned merge decision. Treat every committed suggestion as production code. A green scanner or test suite is a checkpoint—not proof that the design, authorization, or business logic is correct.
Why AI-generated code gets bloated
Code assistants optimize for producing a plausible answer quickly. Without constraints, they may create duplicate helpers, wrap simple operations in unnecessary abstractions, add dependencies for small tasks, repeat validation in several layers, or preserve dead branches because removing them feels risky. Generated tests can reinforce the same mistakes by asserting only the behavior the agent invented.
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Microsoft Learn puts ownership plainly: when an AI agent generates a function, layout, or API call, it becomes your code when you commit it. The guidance also says AI-generated changes need the same testing depth as hand-written code. Cleanup is therefore not a cosmetic pass after generation; it is the engineering process that makes the change maintainable and safe.
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Use separate controls for style, maintainability, security, dependencies, secrets, and behavior. Each catches a different class of defect, and none replaces review of the complete change.
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1. Establish scope, ownership, and a reviewable diff
Label the change and assign one accountable owner
Record that the change was AI-assisted in the pull request or internal change log. Assign a human who can explain the intended behavior, data flow, failure modes, and operational impact. “The assistant wrote it” is not an exception to normal ownership.
Review callers and protected paths
Read the entire diff, then inspect callers, callees, validation boundaries, persistence code, authorization checks, and error handling. A locally sensible function can still bypass a permission check or change the meaning of a shared data structure. Review generated configuration, migrations, infrastructure files, and dependency manifests as part of the same change.
Split large generated pull requests
Large diffs increase reviewer fatigue and make accidental behavior changes harder to spot. Separate mechanical formatting, refactoring, dependency changes, and feature behavior into small pull requests when possible. Keep each change easy to revert and associate tests with the behavior they protect.
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Make formatting deterministic
Run the repository’s formatter at the start of the pipeline and commit its versioned configuration. Pin formatter and linter versions so a local run and CI produce the same result. Formatting noise should not hide a semantic change in review.
Turn recurring comments into rules
Start with rules that remove obvious bloat and expose risky constructs:
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- Unused imports, variables, functions, and unreachable branches.
- Overly broad exception handling that swallows failures.
- Inconsistent naming, style drift, and accidental public APIs.
- Excessive cyclomatic complexity, nesting depth, or function length.
- Unsafe string-built queries, commands, or HTML.
- Duplicated validation and copy-pasted blocks that should share a well-named helper.
Do not enable every rule at maximum severity on legacy code. Establish a baseline for existing findings, then fail new or modified code. Promote repeated human review findings into custom lint rules or reusable helpers; this converts institutional knowledge into a repeatable gate.
3. Measure complexity and duplication separately from legacy code
Generated additions can quietly enlarge maintenance cost even when the repository’s overall metrics look stable. Track new-code complexity and duplication independently from the existing baseline. Review both absolute values and trends across pull requests.
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|---|---|---|
| Branch or cyclomatic complexity | Too many paths for a reviewer or test suite to reason about | Split functions, simplify conditions, and add boundary tests |
| Nesting depth | Control flow hidden inside callbacks, loops, and conditionals | Use guard clauses or extract named operations |
| New-code duplication | Repeated generated blocks that will drift over time | Consolidate only when the shared behavior is genuinely stable |
| Dead-code growth | Unused branches, flags, wrappers, and compatibility layers | Delete after confirming references and deployment assumptions |
One published 2025.3 AI assurance profile uses 3.0% as its maximum duplication for new code and 80.0% minimum new-code test coverage. Treat those values as an example starting point, not a universal law. A project with repetitive generated output may need a stricter duplication ceiling; a safety-critical component may require higher coverage and additional review.
4. Test with an independent source of judgment
Do not let the generating agent grade its own work
OWASP warns that an agent may delete failing tests, weaken assertions, replace real dependencies with mocks, or encode buggy behavior. A passing suite generated by the same agent that produced the implementation provides no independent assurance. Have a human write or review critical tests, or use a separate process and test author.
Add adversarial and boundary cases
- Malformed, empty, oversized, and unexpected-type inputs.
- Expired, revoked, or incorrectly scoped tokens.
- Authorization failures for every protected operation.
- Rate, size, time, and pagination limits.
- Retries, duplicate requests, timeouts, and partial failures.
- Concurrent updates, race conditions, and transaction conflicts.
- Cryptographic edge cases, invalid encodings, and key-rotation paths.
- Real dependency behavior where mocks could conceal integration defects.
Inspect suspicious test changes
Review deleted tests, reduced assertion counts, newly introduced mocks, broad snapshots, and tests that merely restate the implementation. A test should describe an externally meaningful requirement, not prove that generated code returns the value it was designed to return.
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5. Add static, dependency, secret, and infrastructure analysis
Static application security testing
Run SAST on every pull request. Look for injection, unsafe deserialization, path traversal, authorization gaps, insecure randomness, and error paths that disclose sensitive data. Tools such as CodeQL and Semgrep support repository-specific rules; use custom patterns for the risks your framework and architecture actually have.
Software-composition analysis
Audit every AI-suggested dependency instead of accepting a generated package list. Confirm that each package is necessary, maintained, correctly licensed for your project, and pinned to a known version. Fail CI on known vulnerabilities according to your risk policy, and check records such as the National Vulnerability Database, GitHub Advisory Database, or OSV. Review transitive dependencies, not only the direct package named in the prompt.
Secret scanning
Scan commits and pull requests for API keys, tokens, private keys, connection strings, and accidentally embedded credentials. If a secret is detected, revoke or rotate it; deleting the line is not enough because it may remain in history or logs.
Infrastructure and container checks
For generated Dockerfiles, deployment manifests, and infrastructure-as-code, add the corresponding IaC and container scanners. Check exposed ports, excessive privileges, unpinned images, writable filesystems, missing network restrictions, and credentials passed through build layers.
6. Define merge gates that are measurable and accountable
Block merges on critical security findings, leaked secrets, failed required tests, and quality-gate failures unless an authorized human records an explicit exception with scope, rationale, owner, and expiry. Apply conditions to both new code and the overall project: a new change can interact with serious existing defects outside the current new-code window.
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| Gate | Example policy | Exception evidence |
|---|---|---|
| Formatting and lint | No new errors; warnings require an owner and due date | Why the rule is inapplicable and when it will be fixed |
| Security | No unresolved critical findings or exposed secrets | Threat assessment, compensating control, approver, expiry |
| Reliability | No new high-confidence defects in changed paths | Reproduction analysis and monitoring plan |
| Tests | Required suites pass; new-code coverage meets the project threshold | Why coverage is impractical and which tests replace it |
| Duplication | New-code duplication stays below the team’s ceiling | Why repetition is intentional and how drift is prevented |
| Dependencies | Versions pinned and vulnerability review complete | Risk acceptance with upgrade or mitigation date |
Do not use scanner-green rate as the primary success metric. Scanners rarely understand business intent, workflow invariants, or whether a user is allowed to perform an action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Recommended settings by control area
| Control area | Baseline setting | Stricter option for high-risk code |
|---|---|---|
| Formatter | Version pinned; CI fails on unformatted files | Format-only commits separated from behavior changes |
| Lint | Fail on new errors and selected security rules | Fail on new warnings in protected modules |
| Complexity | Track changed functions and trend per pull request | Lower complexity and nesting thresholds for security-sensitive paths |
| Duplication | Measure new code; use 3.0% as a documented example ceiling | Project-specific lower ceiling for repetitive generated output |
| Coverage | Measure new-code coverage; 80.0% is a documented example minimum | Branch and mutation testing for critical logic |
| SAST/SCA/secrets | Run on every pull request and default branch | Pre-commit secret checks and scheduled full-history scans |
| Review | Human approval from an owner familiar with the affected system | Two-person approval for authorization, cryptography, or financial paths |
Choosing tools and integrations
Compare tools on the dimensions that affect your workflow rather than on the number of findings:
- Language and framework coverage, including generated configuration and templates.
- Custom-rule support and the effort required to maintain rules.
- Pull-request, IDE, and command-line integration.
- Complexity, duplication, new-code baselines, and test-coverage reporting.
- Coverage of SAST, software composition, secrets, IaC, and containers.
- False-positive triage, suppression expiry, and audit history.
- Build-time cost and behavior on monorepos.
- Ability to gate both changed code and the full project.
CodeQL, Semgrep, and Snyk Code are examples of tools teams may evaluate for parts of this stack. Select based on your languages, deployment model, policy requirements, and reviewer capacity. No product can certify that generated business logic is correct.
What the available evidence says about AI-assisted remediation
A Microsoft Research CORE study published for FSE in July 2024 found that 59.2% of revised Python files passed both tool checks and human scrutiny across 52 quality checks. Its ranking method reduced false positives by 25.8% in those Python cases. On Java, 76.8% of files passed static analysis across 10 checks, compared with 78.3% for a specialized repair tool.
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Failure modes and recovery steps
The scanner reports no findings, but the feature is wrong
Re-read the requirement and trace a representative request through authorization, validation, persistence, and response handling. Add a test for the business invariant that the scanner cannot infer.
Coverage rises after tests are generated
Compare assertion quality, not only line percentages. Replace implementation-shaped tests with requirement-based cases, add negative paths, and have a reviewer who did not generate the code approve the suite.
Duplication is just below the gate
Inspect repeated blocks manually. Generated code can vary names while preserving identical logic, which defeats token-based duplication thresholds. Consolidate stable behavior and leave intentionally different behavior separate.
A new dependency fixes a small problem
Remove it if a standard-library or existing project utility solves the need. Otherwise pin the version, review transitive packages and advisories, and document the reason it is required.
A pull request is too large to review
Stop the merge, separate formatting and mechanical edits, and divide behavior changes by component or risk. Re-run all gates on each resulting change.
Quick Recap
A practical pull-request checklist
- Mark the change as AI-assisted and name the human owner.
- Read the complete diff, including tests, manifests, generated configuration, and deletions.
- Run the pinned formatter and linter; fix or explicitly justify new findings.
- Check complexity, nesting, dead code, and new-code duplication.
- Run tests authored or substantially reviewed independently of the generating agent.
- Add malformed-input, boundary, authorization, concurrency, and cryptographic cases where relevant.
- Run SAST, dependency, secret, IaC, and container scans applicable to the repository.
- Verify versions are pinned and advisories are reviewed.
- Confirm all merge gates for both new and overall code.
- Record any exception with an accountable approver, mitigation, and expiry date.
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