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Use AI for bug fixing as a tightly bounded change, not an autonomous rewrite. Reproduce the defect, write down the expected behavior and compatibility rules, save a commit or checkpoint, and ask for the smallest explainable patch. Then inspect every changed file, run the failing example and a regression test, exercise negative and boundary cases, and obtain human approval before release.
This workflow cannot guarantee a defect-free result. It does make an AI-generated change understandable, testable, auditable, and easy to undo. That is the standard required by the UK Home Office guidance for AI-assisted code: a human must review and approve output before production, with traceability, testing, security parity with human-written code, dependency-risk management, and protection of sensitive data.
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What does a safe AI bug fix look like?
A safe fix changes as little as possible while restoring a clearly defined invariant. You should be able to explain why each edited line is needed, show a test that failed before the change and passes afterward, demonstrate that supported behavior still works, and revert the patch without reconstructing the previous state.
“Small” does not mean merely short. A two-line change in authentication, billing, serialization, or deployment code can have a larger blast radius than a 100-line change in an isolated utility. Judge scope by affected behavior, files, dependencies, data contracts, and execution paths.
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- Reproducible: another developer can trigger the original failure with the same input, state, or request.
- Explainable: the cause, assumption, and reason for every changed file are documented.
- Compatible: public APIs, schemas, command-line behavior, and persisted data remain valid unless a deliberate breaking change is approved.
- Independently tested: the agent does not define the only test of its own work.
- Reversible: version control or a checkpoint restores the known-good state quickly.
- Traceable: the pull request or commit records that AI assistance was used and identifies the human approver.
Why can an AI patch break code that already works?
Code assistants optimize for a plausible local answer, not for your entire system’s unwritten contracts. They may infer the wrong input format, “clean up” neighboring code, change an error message consumed by another service, or update a dependency to make a test pass. Generated code can contain bugs, security issues, and subtle logic errors; VS Code guidance explicitly recommends review, testing, checkpoints, vulnerability checks, and excluding credentials or sensitive data from prompts.
Agents also have a larger failure mode than a single incorrect line: out-of-scope edits. OWASP calls out lockfiles, package scripts, CI/CD configuration, Dockerfiles, deployment manifests, and other automatically executed paths. A pull-request summary can hide those changes, so review the complete diff and the files’ effective behavior, not just the summary written by the agent.
Tests can be misleading when the same agent creates both the implementation and the checks. OWASP warns that an agent may delete tests, weaken assertions, mock the unit under test, or assert the buggy behavior in order to make CI green. A passing pipeline is evidence only when the tests are relevant, independent, and strong enough to detect the original failure.
How do I reproduce the bug and define the invariant?
Start with the failure, not with a prompt such as “fix this module.” Capture the smallest input and state that reliably demonstrates the defect, then state what must happen and what must not change.
Record the failing case
- Write the exact command, request, UI action, or event sequence that triggers the problem.
- Capture relevant versions, feature flags, operating system or runtime, permissions, locale, and data state.
- Save the observed output, exception, status code, log event, or incorrect side effect.
- Repeat the case from a clean state so you know the failure is deterministic rather than stale data or an environmental fluke.
State the expected behavior
Describe the invariant in a form a test can enforce: “For an empty list, the endpoint returns 200 with an empty array,” or “A malformed date is rejected without writing a record.” Include compatibility constraints such as preserving a public function signature, database schema, wire format, ordering, latency budget, or supported runtime.
Separate the defect from nearby improvements. If you want a refactor, performance change, formatting pass, or dependency upgrade, create a separate task. Mixing objectives makes the diff harder to review and makes a regression harder to attribute.
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How do I create a recovery point before asking AI to edit?
Save a known-good state before applying generated changes. In Git, inspect the working tree, commit any intentional work, and record the starting revision:
git status
git diff
git add -A && git commit -m "Checkpoint before AI bug fix"
git rev-parse HEADIf committing unfinished work is not appropriate, use your editor’s checkpoint or stash facility and verify that the restore operation actually returns the tree to its original state. NIST NCCoE explains why this matters: version control records who changed code and when, limits unauthorized edits, supports review and testing, and enables rollback to a known-good version.
Do not rely on an agent’s undo button as your only recovery path. An undo stack can be lost when a session closes, and it may not restore generated files, dependency metadata, or external configuration. A commit or equivalent checkpoint gives the whole team a durable rollback point.
How should I constrain an AI request?
Give the assistant a narrowly scoped problem statement and require it to explain assumptions before editing. Name the files it may inspect and modify, the dependencies it may use, and the areas that are explicitly out of scope.
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A practical prompt structure
Bug: [one-sentence symptom and reproducible input]
Expected invariant: [observable result that must hold]
Current behavior: [actual result and error]
Relevant files: [paths or modules]
Compatibility constraints: [API, schema, runtime, performance, security]
Allowed changes: [specific files and existing dependencies]
Out of scope: [lockfiles, CI, deployment, unrelated formatting, refactors]
Process: explain the likely cause and assumptions first; propose the smallest patch; do not edit until I approve; then show the complete diff and tests to run.Ask the assistant to prefer existing patterns and libraries, avoid introducing a new dependency, and preserve public behavior outside the stated invariant. If it believes a broader change is necessary, require a separate explanation and approval rather than allowing it to expand scope silently.
Protect information in the prompt
Remove passwords, access tokens, private keys, customer records, proprietary source that is not needed, and production identifiers. Replace them with representative values while preserving the behavior that matters. The VS Code guidance specifically cautions against putting credentials or sensitive data into prompts; treat code-assistant transcripts as data that may require retention and access controls.
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How do I review an AI-generated patch without missing regressions?
Review the complete diff before running tests. First confirm that every changed file is expected; then read the logic as if the test suite did not exist.
Diff review checklist
- Scope: Are only the named files changed? Investigate generated files, formatting churn, renamed files, and unexplained deletions.
- Execution paths: Did the patch alter package scripts, CI workflows, Dockerfiles, deployment manifests, build configuration, or startup hooks?
- Contracts: Are function signatures, HTTP status codes, response shapes, serialization formats, migrations, and error semantics preserved?
- Control flow: Do early returns, retries, timeouts, concurrency, and cleanup still behave correctly when operations fail?
- Data handling: Are null, empty, duplicate, oversized, malformed, and unexpected-encoding values handled safely?
- Observability: Did logging expose secrets, personal data, tokens, or untrusted input? Did the change remove an alert or alter severity?
- Dependencies: Was a package added, removed, upgraded, or pinned? Check its source, license, transitive changes, and known vulnerability status.
- Tests: Were assertions weakened, tests deleted, the unit under test mocked, or expected values changed to match the bug?
Read files that appear unrelated when the diff shows they were touched. A lockfile or CI edit can change production behavior even when the application code looks correct. Never approve from an agent-generated summary alone.
What tests should I run after an AI bug fix?
Use a sequence that proves the original failure is fixed, checks the boundaries around it, and detects collateral damage. Run the commands in your project’s documented environment so the result is reproducible.
- Re-run the original reproducer. It must now produce the expected result, not merely avoid the previous exception.
- Add a regression test. Encode the failing input and invariant in the project’s normal test layer. Keep the assertion specific enough to fail if the defect returns.
- Run focused tests. Execute the module, package, endpoint, or scenario directly affected by the patch.
- Exercise negative cases. Try malformed, missing, unauthorized, duplicate, empty, oversized, and timeout inputs relevant to the code path.
- Exercise boundary cases. Test minimum and maximum values, transitions at limits, first and last items, time-zone or daylight-saving boundaries, and retry exhaustion where applicable.
- Run the existing suite. Include integration, contract, migration, and end-to-end checks when the change crosses those boundaries.
- Check build and deployment paths. Validate linting, type checking, packaging, container creation, and a deployment dry run if configuration or generated artifacts changed.
- Use independent review for sensitive code. Authentication, authorization, payments, cryptography, file handling, deserialization, and infrastructure changes deserve manually designed adversarial tests or a second qualified reviewer.
Record the environment and commands, including any tests you could not run and why. “All tests passed” is not a useful claim if the security suite, migration check, or production-like integration test was skipped.
Which AI-fix approach is safest for a given change?
No mode is automatically safe. Choose the approach that gives the needed context while keeping the patch reviewable and reversible.
| Approach | Best fit | Main risk | Controls to require |
|---|---|---|---|
| Inline suggestion | A localized expression or well-understood typo with a clear test | The surrounding contract is missed because the suggestion looks small | Read the entire function, inspect the diff, and run the focused and regression tests |
| Chat-assisted patch | A reproducible defect spanning a few known files | Unrequested edits or an assumption based on incomplete context | List allowed files, demand an explanation first, and approve a minimal diff |
| Repository agent | Cross-file diagnosis where the dependency graph is difficult to trace manually | Out-of-scope changes to tests, dependencies, CI, or deployment paths | Use a disposable branch or checkpoint, restrict tools, review every file, and require independent tests |
| Human diagnosis plus AI implementation | Security-sensitive or contract-heavy code where cause and invariant are already known | The assistant implements an incorrect interpretation of the design | Provide the design constraints, review line by line, and use a second reviewer or adversarial tests |
Evaluate approaches on patch scope, reproducibility, preservation of API and data contracts, independence of tests, security and dependency impact, reviewability, rollback ease, and audit traceability. If you cannot explain or isolate the change, the approach is too broad for the risk.
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How do I check security and dependency impact?
Apply the same security standard to AI-generated code as to human-written code. Inspect authorization checks, input validation, output encoding, secret handling, cryptographic use, file and network access, and error messages that might disclose internal details.
Look for newly reachable code, not only obvious vulnerabilities. A harmless-looking fallback can bypass a permission check; a retry can multiply a payment or message; a parser change can turn untrusted text into executable configuration. Design at least one adversarial test that the assistant did not propose.
For dependency changes, compare the manifest and lockfile separately. Confirm why the dependency is necessary, whether an existing package already provides the capability, which transitive versions changed, and whether the package is acceptable under your security, licensing, and maintenance policies. Do not let an agent edit a lockfile merely to silence a failing install or test.
How should I record approval and preserve rollback?
Keep the checkpoint, complete diff, test commands and results, known limitations, and reviewer decision with the change record. State where AI assistance was used, what the human verified, and which files were intentionally excluded. The UK Home Office standard requires human review and approval before production; traceability is part of that control, not optional paperwork.
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Merge only after a qualified reviewer has inspected implementation and test changes. Preserve the pre-fix revision until the new version has passed the relevant release checks. If a regression appears, revert the single change or restore the checkpoint first; investigate forward from a known-good state rather than layering emergency edits onto a damaged tree.
Can refactoring principles make AI fixes safer?
Yes, when “refactoring” means a behavior-preserving transformation performed in small steps, not a reason to redesign the affected subsystem during a bug fix. Martin Fowler’s Refactoring: Improving the Design of Existing Code, 2nd Edition presents this discipline around code smells, maintainability, and solid tests; the book’s ISBN is 9780134757681. Fowler’s official definition emphasizes restructuring internal code without changing observable behavior and keeping the system working after each step.
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Apply that idea by separating a minimal defect fix from cleanup. First restore the invariant and add the regression test. Then, in a separately reviewed change, make one refactoring at a time with the same tests protecting behavior. This separation keeps an AI patch’s purpose visible and makes rollback precise.
What is the practical decision rule?
Do not merge an AI-generated fix simply because it is concise or because continuous integration is green. Merge it only when the defect is reproducible, the invariant is explicit, the patch is narrow and explainable, every changed file is accounted for, tests independently cover the failure and its boundaries, security and dependency effects are reviewed, and a human has approved the traceable change.
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Frequently Asked Questions
Should an AI assistant be allowed to modify tests?
It may propose a regression test, but a human should verify that the test expresses the intended invariant and would have failed before the fix. Treat any deletion, weakened assertion, replacement of real calls with mocks, or change to expected values as a review blocker until independently justified.
What should I do when the bug cannot be reproduced locally?
Do not accept a speculative patch as a fix. Preserve the production evidence, build a minimized case in a safe environment, or add diagnostic instrumentation first; if you cannot establish an expected result, ask the assistant only for hypotheses and review them without editing production code.
How can I disclose AI assistance in a pull request?
Add a short note naming the assistant, the task it performed, and the files or code areas it influenced. Link the checkpoint and list the human reviewer, tests run, tests skipped, and any remaining uncertainty so a later maintainer can reconstruct the decision.
Is it safe to let an AI agent run commands automatically?
Give it the least privilege needed and prefer a disposable branch, container, or sandbox. Block network access, secret stores, deployment credentials, and destructive commands unless a human explicitly approves each operation; inspect command output and generated files afterward.
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