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Yes—you can give an AI reviewer enough repository context without uploading the whole repository. Context slicing selects files and symbols related to a pull request, adds the project rules, interfaces, tests, and security guidance that explain those changes, and places that material ahead of the diff. The reviewer then reports only evidence-backed, actionable findings, while project tests and a maintainer make the final decision.
A practical free-tier setup combines path-aware rules with staged review effort: use a cheap local pass for ordinary changes, escalate security-sensitive or cross-component patches, and verify every finding. Mira provides a self-hosted pull-request service, Qwen Code supports local reviews at several effort levels, and GitHub Copilot code review offers hosted repository context on paid plans.
What is context slicing, and why does it improve AI code review?
Context slicing is a way to select repository material by what a patch actually changes instead of sending an entire checkout to a model. The slice normally contains the changed files, their public interfaces, nearby tests, component documentation, configuration defaults, and security guidance. Unrelated directories stay out of the prompt.
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The slice is not proof of correctness. It is a bounded evidence package. The model should cite an exact file and line, explain the violated behavior, and state its confidence. Linters, tests, targeted security checks, and a human maintainer still decide whether a finding is valid.
How does a context-sliced review work from pull request to decision?
The workflow has seven stages. Keep the stages deterministic so that two reviews of the same patch receive the same baseline context.
- Resolve the patch. Identify the pull request base commit, head commit, and exact diff. Do not review a moving branch without recording which base was used.
- List changed paths and file types. Capture added, modified, deleted, and renamed paths. File type matters: a migration, test, configuration file, and documentation edit need different rules.
- Match review rules. Apply path patterns to the changed paths. A component rule can match any changed file under a directory, while a patch-shape rule can require that all changed files fit a category such as tests-only or docs-only.
- Load the smallest useful slice. Fetch the matching component guidance, public interfaces, neighboring tests, security checklists, and relevant configuration defaults. Prefer a few explanatory files over an entire directory.
- Place context before the diff. Mark the loaded material as trusted context, then present the patch. Mozilla Firefox Source Docs describes this pattern for its Review Helper: it fetches
review-context.toml, matches rules against changed files, fetches the referenced skill or documentation files, and injects them as trusted<context>blocks ahead of the diff. - Request constrained findings. Ask for actionable defects only, with exact file-and-line evidence, impact, confidence, and a distinction between a bug and a style suggestion.
- Verify and gate. Run tests, linters, type checks, and targeted security checks. A maintainer confirms that each finding is real and that any fix preserves the project’s intent before merging.
How should path-aware review rules be designed?
Rules should answer two questions: which changed paths activate a rule, and which files should be loaded when they do. Keep rules close to the code they govern, and make broad rules explicit so an unrelated file does not pull in an enormous prompt.
Use component rules for local behavior
A rule for src/auth/ might load the authentication contract, token-validation guidance, and the tests beside that component. A rule for db/migrations/ might load migration ordering policy and rollback tests. These are any-file rules: one matching path is enough to activate them.
Use all-file rules for patch shape
An all-files rule applies only when every changed path meets a condition. It can classify a pull request as documentation-only, tests-only, or generated-code-only and prevent unnecessary implementation guidance from being loaded.
Illustrative rule layout
The exact fields depend on the Review Helper implementation, but the structure below shows the decisions a repository should encode. Treat it as a design example, not a universal schema.
# review-context.toml
[[rules]]
name = "authentication-guidance"
any_file = ["src/auth/**", "tests/auth/**"]
context = ["docs/auth-contract.md", "docs/security-checklist.md", "tests/auth/fixtures.md"]
[[rules]]
name = "migration-safety"
any_file = ["db/migrations/**"]
context = ["docs/migrations.md", "tests/migrations/rollback.md"]
[[rules]]
name = "documentation-only"
all_files = ["**/*.md", "docs/**"]
context = ["CONTRIBUTING.md"]Store the rule file under version control, review changes to it like code, and avoid silently loading secrets, production credentials, or unrelated private documents. A rule should explain behavior, not merely repeat the filename.
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What belongs in the review slice?
Load only material that can change the interpretation of the patch. The following categories cover most open-source projects.
| Slice item | Why it matters | Typical selection method |
|---|---|---|
| Changed files and symbols | Defines the exact code under review. | Diff paths plus the functions, classes, or exported symbols they modify. |
| Public interfaces | Shows callers’ expectations, accepted values, and compatibility promises. | Interface definitions, schemas, API contracts, or exported type declarations referenced by the patch. |
| Neighboring tests | Reveals edge cases and the project’s intended behavior. | Tests covering the changed component and fixtures used by the touched functions. |
| Component documentation | Explains invariants that are not obvious in implementation code. | Documentation matched by the component path rule. |
| Security guidance | Provides explicit checks for authentication, authorization, parsing, secrets, and unsafe output. | Security checklist selected when sensitive paths change. |
| Configuration defaults | Prevents a reviewer from assuming defaults that differ between development and production. | Only the configuration files and documented defaults consumed by the changed code. |
| Patch metadata | Clarifies intent and scope. | Pull-request description, linked issue summary, and base commit, after removing irrelevant conversation. |
Set a file-count and token budget appropriate for the model, then shrink the slice when the explanation still works without a file. A smaller, relevant context is easier to audit and less likely to bury a real defect.
Which free-tier or low-cost options fit this workflow?
There is no single best deployment. Choose according to your data boundary, integration needs, and how much verification you want on each pass.
| Option | Context coverage | Hosting and integrations | Cost and access | Useful controls |
|---|---|---|---|---|
| Mira | Indexes the whole repository and combines that index with the pull-request patch. | Self-hosted; connects to GitHub, GitLab, and Forgejo; posts inline comments and walkthroughs. | Documentation describes it as fully open source, Apache 2.0 licensed, with no paid tier or license key. You still provide compute and an LLM provider. | Confidence thresholds, deduplication, severity sorting, and per-pull-request comment caps. |
| Qwen Code | Local staged review of changes, pull requests, or individual files; you control the slice you provide. | Command-line workflow on the local checkout. | Effort level determines how much analysis and verification you request; model and provider charges, if any, depend on your setup. | /review targets, plus --comment, --fix, and --resume modes. |
| GitHub Copilot code review | Hosted review with agentic capabilities that gather full-project context. | Pull-request integration on GitHub; organization policies affect availability. | Available on paid Copilot plans. GitHub estimates $0.05–$1 in AI credits for Lite and $0.25–$5 for Balanced; these are estimates that can change, and GitHub Actions minutes may add cost. | Lite and Balanced effort levels, with fixes that can be applied from review comments. |
These options are complementary. Mira is a good fit when code must remain in infrastructure you operate. Qwen Code is useful for a local first pass or an offline-oriented process. Copilot is convenient when the project already uses GitHub and accepts its hosted data boundary and paid-plan requirements.
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How do I run a staged local review with Qwen Code?
Use Qwen Code to start with a narrow local review, then spend more analysis only where the patch warrants it. Its documented review targets cover local changes, pull requests, and individual files.
- Prepare a clean base. Check out the pull request head, record the base commit, and ensure generated files and unrelated local edits are excluded.
- Start with triage. Invoke the
/reviewtarget for the change or pull request at low effort. The low level is intended for quick analysis and is explicitly described as unverified; do not treat its output as a merge decision. - Ask for evidence. Require each item to include a file, line, violated behavior, practical impact, and confidence. Reject comments that cannot point to the patch or its loaded contract.
- Escalate selectively. Re-run at medium effort when the change affects normal behavior that needs build or test verification. Use high effort for broad, security-sensitive, or cross-component patches where parallel analysis and iterative verification are valuable.
- Choose an output mode. Use
--commentwhen you want review comments,--fixonly after a maintainer accepts a finding, and--resumeto continue a review rather than starting from a different context. - Run the project checks yourself. A model’s verification pass does not replace the repository’s required test, lint, type, or security commands.
Keep the low-effort pass as a filter, not as a lower standard. A change that touches authentication, parsing, concurrency, data migrations, or a cross-service contract should move to a deeper pass even when the first review is quiet.
How do I self-host Mira for pull-request reviews?
Mira is designed for maintainers who want repository indexing and pull-request comments without a paid service tier. Plan for Docker, an LLM provider, storage for the index, and access to the forge where reviews will be posted.
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- Choose the deployment boundary. Run Mira in infrastructure allowed to read the repository and send prompts to the LLM provider you select. Document what code leaves that boundary.
- Connect the forge. Configure the GitHub, GitLab, or Forgejo connection with the least privilege needed to read pull requests and publish comments.
- Index the repository. Let Mira build its repository index, then refresh it when major branches or generated interfaces change so retrieved context matches the code under review.
- Set noise controls. Configure confidence thresholds, deduplication, severity ordering, and a per-pull-request comment cap. A cap keeps a single ambiguous patch from overwhelming maintainers.
- Run a pilot queue. Start on a small set of repositories or labels, compare findings with maintainer decisions, and adjust thresholds before making comments automatic.
- Require human approval. Keep merge protection and maintainer review independent from the bot. An indexed repository improves retrieval; it does not establish that a suggested fix is safe.
Mira’s self-hosted, open-source model removes a license-key gate, but it does not remove operating costs or provider policy decisions. Budget for compute, logs, storage, and the model calls generated by your review volume.
What should I know about hosted GitHub Copilot code review?
Copilot code review is the convenient hosted path for teams already working in GitHub, but it is not a free-tier deployment. GitHub describes it as reviewing pull requests, identifying issues, and suggesting fixes that can be applied in a few clicks.
Access depends on a paid Copilot plan and organization policies. GitHub documents two effort levels: Lite and Balanced. Its estimates put Lite at $0.05–$1 in AI credits and Balanced at $0.25–$5 per review, with additional GitHub Actions-minute considerations. Treat those figures as estimates, not a fixed price list.
Use the hosted option when the project’s governance permits repository processing by the service and the convenience of automatic pull-request integration outweighs the recurring credit usage. If the code cannot leave your controlled infrastructure, use a self-hosted or local design instead.
How should review effort be staged to control noise and cost?
Spend the least analysis that can answer the question, then escalate based on risk rather than file count alone.
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|---|---|---|---|
| Quick triage | Small, isolated changes with familiar tests. | Diff plus the minimum matched context; actionable findings only. | The patch changes security boundaries, parsing, concurrency, migrations, or an external contract. |
| Verified review | Normal feature work or a change with meaningful test impact. | Build or test execution and one verification pass in addition to context analysis. | Tests are flaky, the change crosses components, or the model reports conflicting evidence. |
| Deep review | Security-sensitive, high-impact, or cross-service patches. | Broader parallel analysis, iterative verification, and targeted security checks. | Only a human design review can resolve the remaining uncertainty. |
This staging pattern maps naturally to Qwen Code’s low, medium, and high effort levels. For hosted tools, select the least expensive effort that still supplies the required verification, and record why a deeper level was chosen.
What prompt produces useful, low-noise findings?
A good prompt defines an evidence standard and a stopping rule. It should prevent the model from turning preferences into defects.
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Review only the supplied patch and trusted context.
Report actionable defects that could affect correctness, security, reliability,
compatibility, or data integrity.
For each finding, provide:
- exact file and line
- violated contract or expected behavior
- concrete impact
- confidence: high, medium, or low
- a minimal fix direction
Do not report style preferences, hypothetical issues without a code path,
or problems contradicted by the supplied tests or documentation.
If no actionable defect is supported, say so.Ask the model to separate a defect from a style suggestion and to quote the relevant contract in its reasoning. Require it to acknowledge missing context instead of filling gaps with assumptions. Keep prompts stable across runs so changes in output reflect the patch, not a different instruction.
How do I verify an AI finding before acting on it?
Verification is a short investigation, not a vote on whether the wording sounds plausible.
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- Reproduce the path. Read the cited lines and trace the input, state, or error path that the finding claims is broken.
- Check the contract. Compare the claim with the matched interface, documentation, configuration default, and neighboring tests.
- Add or run a focused test. Exercise the reported edge case, including malformed, unauthorized, concurrent, or rollback scenarios where relevant.
- Run required project checks. Use the repository’s tests, linter, formatter, type checker, and targeted security tooling. A passing test suite does not disprove every finding, but a failing check is concrete evidence.
- Review the proposed fix. Ensure it does not break compatibility, alter migration order, weaken authorization, or change an intentional behavior.
- Record the disposition. Mark the finding as fixed, accepted risk, duplicate, false positive, or needs design discussion. This record helps tune thresholds and rules.
There is no authoritative accuracy or time-saved statistic in the available documentation for these workflows. Measure your own accepted-finding rate and false-positive rate instead of promising a universal detection percentage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What causes noisy or hallucinated review comments?
Most noise comes from missing contracts, oversized context, or an unconstrained output request. Diagnose the symptom before changing the model.
| Symptom | Likely cause | Correction |
|---|---|---|
| Comments concern files untouched by the patch. | The prompt contains a whole repository or the rule matcher is too broad. | Restrict retrieval to matched paths and explicitly require a changed-line or dependency path. |
| The reviewer misses a required invariant. | Component documentation, interface definitions, or neighboring tests were omitted. | Add a path-specific context rule and keep the invariant close to the component. |
| Every style preference appears as a defect. | The prompt does not define actionable findings. | Require impact, evidence, confidence, and a distinction between bug and style suggestion. |
| Duplicate comments repeat the same issue. | Several retrieval paths expose the same behavior or parallel reviewers lack deduplication. | Enable deduplication and cap comments per pull request; merge findings by root cause. |
| A fix compiles but breaks behavior. | The model was allowed to edit before tests or maintainer review. | Use a comment-only pass first, then apply a minimal fix and run targeted tests. |
| Review cost grows unexpectedly. | Every patch receives the deepest effort or an oversized context slice. | Use low effort for triage, escalate by risk, and cap files and tokens. |
| Private code appears in an unexpected service. | The hosting or LLM data boundary was not documented. | Choose local or self-hosted deployment where required and audit provider settings before enabling reviews. |
How do I choose between local, self-hosted, and hosted review?
Make the decision with three constraints: where code may be processed, how much integration you need, and whether recurring usage charges are acceptable.
- Choose local staged review when a maintainer wants direct control of the checkout, can run tests locally, and prefers to spend deeper analysis only on risky patches.
- Choose self-hosted pull-request review when multiple repositories need automatic comments across GitHub, GitLab, or Forgejo and the team can operate Docker, an index, and an LLM provider.
- Choose hosted review when GitHub integration and minimal operations matter more than a paid-plan requirement and the organization approves the service’s repository data boundary.
- Combine them when a local low-effort pass handles every patch while a self-hosted or hosted service performs a deeper review only after risk-based labeling.
Do not treat repository indexing as a substitute for path-aware rules. An index can retrieve relevant material, but deterministic matching makes the context explainable and easier to audit.
What security and governance checks belong in the workflow?
AI review introduces an additional data-processing path, so governance belongs beside technical configuration.
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- Define which repositories, branches, issues, generated artifacts, and secrets the reviewer may read.
- Exclude credentials, production dumps, private keys, and unrelated customer data from indexing and prompts.
- Use least-privilege forge tokens and rotate them according to project policy.
- Log the base commit, matched rules, context files, model effort, findings, and verification result for each review.
- Pin or record the reviewer version and prompt so a later rerun is comparable.
- Require a maintainer to approve automated fixes and preserve branch protection independent of the reviewer.
- Review rule-file changes as security-sensitive configuration because a broad pattern can expose far more code than intended.
What is a practical rollout checklist?
Use this sequence to move from an experiment to a dependable review gate.
- Pick one repository and document its pull-request base and test commands.
- Create path rules for the highest-risk components, beginning with authentication, parsing, migrations, and public interfaces.
- Identify the smallest documentation, interface, test, and security files that explain each component.
- Set a context size cap and a comment cap before enabling automatic output.
- Run a local low-effort review on several historical patches and label false positives.
- Add medium or high effort only for patches that meet explicit risk criteria.
- Connect Mira or a hosted service to a limited repository or label if automatic comments are needed.
- Require exact location, impact, confidence, and defect-versus-style classification in every finding.
- Run project verification and have a maintainer disposition each finding.
- Measure accepted findings, false positives, duplicate rate, review latency, and usage cost, then refine rules rather than simply increasing model effort.
The result is a review system that is selective by design: enough context to understand a change, not enough unrelated code to drown it out, and enough verification to keep an AI suggestion from becoming an accidental merge decision.
Frequently Asked Questions
Can context slicing work for monorepos?
Yes. Define rules around service, package, or component boundaries and add cross-service contract files only when a changed interface requires them. Keep shared schemas and generated clients in explicit rules so a local change does not automatically retrieve every package.
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Usually yes, when their removal changes an interface, migration path, permission rule, or test fixture. A deleted file can explain why callers fail even though it no longer exists in the head revision, so retain the relevant base-version content with clear revision labels.
How can maintainers stop an AI reviewer from commenting on generated code?
Mark generated paths in the rule system and require the reviewer to inspect the source or generator instead. An all-files rule can identify generated-only patches, while a component rule can load the generator contract when generated output changes alongside source code.
Is an AI review suitable as the only approval for a security patch?
No. Security-sensitive changes need human threat modeling, targeted tests, and the project’s normal security approval process. AI output can provide additional hypotheses, but it cannot establish that an exploit is impossible.
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