AI Code Review Tools: GitHub Copilot vs Cursor vs Tabnine (2025)

AI assistants now participate in code reviews, not just autocomplete. In 2025, developers expect help beyond suggestions: context awareness across files, actionable review comments, security findings, refactoring plans, and tests that actually run. We evaluated three leading options—GitHub Copilot, Cursor, and Tabnine—across real repositories to understand where each shines.

This review focuses on production outcomes: how fast you ship better code with fewer bugs. We measure code quality, security, developer experience, latency, cost, and enterprise controls.

Summary

• Copilot: Best generalist with strong IDE and GitHub integration; excels at inline suggestions and PR summaries; security improving with partner tools.
• Cursor: Best for codebase‑level reasoning and multi‑file edits; outstanding context handling; great for refactors and complex reviews.
• Tabnine: Best privacy posture (local/cloud options) and policy controls; reliable completion with team learning; review capabilities advancing.

Use Copilot if you live in GitHub workflows, Cursor for deep codebase transformations, and Tabnine for strict privacy or on‑prem needs.

Test setup and methodology

We ran the tools against three representative projects:

1. A Next.js 15 app (App Router, RSC) with API routes and analytics
2. A Node.js service with PostgreSQL, Prisma, and message queues
3. A Python FastAPI service with data processing pipelines

For each tool, we measured:

• Review quality: actionable comments, false positives, depth of understanding
• Security findings: OWASP Top 10 classes (injection, auth, XSS, SSRF)
• Refactoring help: multi‑file edits, dead code removal, structure proposals
• Test generation: coverage uplift, correctness
• Latency: time‑to‑first‑useful output, average review cycle
• DX: integration friction, configurability, learning curve
• Privacy & enterprise: data retention, local options, policy controls

How each tool works (today)

GitHub Copilot

Copilot integrates into IDEs for inline suggestions and into GitHub for PR summaries and explanations. With Copilot Enterprise, you get codebase context indexing and model routing, plus security features through GitHub Advanced Security (separate product).

Strengths:

• Exceptional inline suggestions that adapt to your code style
• Natural language queries inside IDE: "explain this function", "write tests"
• GitHub PR integration: summarize changes, highlight risky diffs
• Ecosystem alignment with Actions, CodeQL, Advanced Security

Constraints:

• Multi‑file, large‑scale transformation is limited compared to Cursor
• Heavily cloud‑oriented; privacy depends on plan and settings
• Security findings rely on separate scanning tools for depth

Cursor

Cursor is an AI‑first code editor (built on the VS Code ecosystem) that emphasizes codebase‑wide context and multi‑file changes. You can chat with the editor, ask it to implement refactors, and watch it apply coordinated edits across files with diffs.

Strengths:

• Deep codebase understanding; maintains cross‑file invariants
• Multi‑file edits with clear diffs and rollback; great for migrations
• Conversational refactoring and architecture assistance
• Custom model selection and per‑project memory

Constraints:

• Newer ecosystem; fewer plugins than VS Code/JetBrains
• Learning curve: you get the most by changing habits
• Requires trust to allow automated multi‑file edits

Tabnine

Tabnine focuses on privacy, policy control, and team learning. It offers local (on‑device) and private cloud deployments so code never leaves controlled environments. Review capabilities are growing via policies, completions, and enterprise features.

Strengths:

• Strong privacy and compliance posture (local/private options)
• Team learning without sending code to public models
• Solid completions across languages; lightweight footprint
• Admin policy controls for suggestions and usage

Constraints:

• Review features are less holistic than Cursor’s codebase edits
• Smaller model capacity vs generalist LLMs
• Fewer natural language workflows than Copilot/Cursor

Evaluation results

1) Review quality and depth

We submitted real PRs (bug fixes, features, refactors) and asked each tool to review.

• Copilot produced concise PR comments and decent high‑level feedback. It flagged unclear names, missing tests, and risky patterns (e.g., unguarded nulls) reliably. On complex refactors, it identified risks but rarely proposed multi‑file edits.
• Cursor generated the most actionable feedback. It traced issues across modules (e.g., a change in a schema misaligned with a downstream validator) and proposed diffs to fix them. It also suggested moving logic between layers to reduce coupling.
• Tabnine focused on local suggestions inside files. It flagged common issues (null checks, missing awaits), and when paired with static analyzers, produced a solid baseline review.

Verdict: Cursor wins for complex, multi‑module reviews; Copilot for concise PR commentary; Tabnine for privacy‑first suggestions.

2) Security findings

We planted vulnerabilities: SQL injection risk, XSS via unescaped HTML, insecure JWT verification, and SSRF through arbitrary URL fetch.

• Copilot identified obvious injection patterns and suggested parameterized queries. It sometimes missed context when the vulnerable sink was indirect.
• Cursor tracked tainted data across files better. It located the vulnerable sink in a utility and proposed a safe helper function used in all call sites.
• Tabnine flagged risky patterns inline; deeper findings depended on pairing with SAST tools.

Verdict: Cursor for flow‑aware findings; Copilot close behind; Tabnine benefits most from a SAST companion.

3) Refactoring and migrations

Scenario: migrate a custom date utility to date-fns, remove moment.js, and adjust all call sites; then split a large service into smaller modules.

• Copilot assisted file‑by‑file, offering migration hints and snippets.
• Cursor executed coordinated multi‑file edits, created helper wrappers, and updated imports across the tree. It produced a migration plan and applied it safely with diffs.
• Tabnine helped with snippets and suggestions; the bulk lift remained manual.

Verdict: Cursor by a wide margin for refactors and migrations.

4) Test generation and coverage uplift

We asked each tool to generate tests for HTTP handlers and a React component with stateful logic.

• Copilot produced the most consistent tests that ran on first try; coverage uplift averaged +18% in our projects.
• Cursor generated comprehensive tests and, importantly, adjusted the code to improve testability when we asked. Occasional flakiness required edits.
• Tabnine produced helpful scaffolds; needed more manual finishing.

Verdict: Copilot wins on first‑try success; Cursor close with better architecture guidance.

5) Latency and throughput

• Copilot: near‑instant inline suggestions; PR analysis in seconds.
• Cursor: slightly higher latency for codebase‑wide actions but still fast; multi‑file proposals are worth the wait.
• Tabnine: very fast local completions; review‑like workflows vary by setup.

6) Privacy, policy, and enterprise

• Copilot Enterprise: better controls, retention policies, model routing, and integration with GitHub enterprise features.
• Cursor: project‑level memory and custom models; relies on cloud for most features.
• Tabnine: strongest privacy (local/private) and admin controls; ideal for regulated environments.

Hands‑on workflows

Copilot PR Review + Tests

1. Create PR → ask Copilot to summarize changes and risks.
2. Accept comments → prompt: "Generate Jest tests for edge cases X, Y".
3. Iterate inline with Copilot Chat until tests pass.

Pros: fast feedback loop. Cons: limited cross‑module refactors.

Cursor Codebase Refactor

1. Open chat: "Migrate moment.js to date-fns across repo; create a compatibility layer, then remove it."
2. Review proposed diffs → accept in batches.
3. Ask for architecture notes and follow‑up cleanups.

Pros: deep, coordinated changes. Cons: requires trust and review discipline.

Tabnine Privacy‑First Flow

1. Enable local model and team learning.
2. Use Tabnine for suggestions; run SAST in CI (e.g., CodeQL/Snyk).
3. Combine human review with Tabnine hints.

Pros: strong privacy; good baseline. Cons: fewer NL workflows.

Comparison table

Capability	Copilot	Cursor	Tabnine
Inline suggestions	Excellent	Great	Good
PR review comments	Great	Good	Fair
Multi‑file refactor	Fair	Excellent	Fair
Security findings	Good	Great	Fair
Test generation	Excellent	Great	Good
Latency	Excellent	Good	Excellent (local)
Privacy controls	Good	Good	Excellent
Enterprise readiness	Excellent	Good	Great

Pricing (indicative)

• Copilot Individual: ~$10/mo; Business: ~$19/user/mo; Enterprise varies
• Cursor Pro: ~$20/mo; Business: ~$40/user/mo
• Tabnine Pro: ~$12/mo; Enterprise: custom; local/private options

Always check current prices and enterprise feature matrices.

Recommendations by team size

Solo/Small teams (1–10 devs)

• Copilot or Cursor as primary; add SAST (free tiers) for security.
• Prioritize speed and breadth of suggestions.

Growth teams (10–50 devs)

• Cursor for migrations and codebase hygiene + Copilot for PRs.
• Add policies and review checklists; standardize test templates.

Enterprise/Regulated

• Tabnine for privacy + Copilot Enterprise for GitHub integration.
• On‑prem or private deployments; strict retention policies.

Implementation playbook (practical)

1. Define code review standards and a rubric (security, performance, maintainability, tests).
2. Configure your chosen tool(s) and set policies (data, prompts, scope).
3. Start with one team as a pilot; gather metrics (PR time, bugs, coverage).
4. Create prompt templates for reviews, tests, and refactors.
5. Roll out with training; pair programming to level up usage.
6. Review monthly; tune prompts, policies, and tool mix.

Prompt templates you can reuse

System: You are a senior reviewer. Review changes for security, correctness, performance, and maintainability.
Instruction: Provide actionable comments with file/line refs; suggest diffs where helpful. Flag security risks using OWASP categories. Require tests for new logic.

System: You are a senior QA engineer. Generate Jest tests covering edge cases, error paths, and concurrency. Include setup/teardown and meaningful assertions.

System: You are a refactoring assistant. Propose a stepwise plan to migrate library X→Y, provide compatibility wrappers, and generate multi‑file diffs preserving behavior.

Limitations and caveats

• Hallucinations exist; never auto‑merge without human review.
• Models can miss contextual security issues; keep SAST/DAST.
• Multi‑file edits require careful diff review and tests.

Conclusion

• Pick Copilot if you want the smoothest PR experience and best all‑around coding companion.
• Pick Cursor if you need codebase‑level reasoning and multi‑file refactoring strength.
• Pick Tabnine if privacy and on‑prem control are non‑negotiable.

Many teams benefit from a hybrid: Cursor for migrations, Copilot for PRs/tests, and Tabnine for private codebases. Measure outcomes (PR cycle time, defects, coverage), enforce review discipline, and iterate on prompts and policies. AI review is not a replacement for engineering judgment—it’s a power multiplier when used with rigor.

Appendix A — Evaluation Framework

Goals

• Improve code quality, reduce defects, and accelerate review cycles.
• Increase test coverage without sacrificing maintainability.
• Enhance security posture through earlier detection of risky patterns.

Dimensions and Rubric (0–5)

• Review correctness: factual accuracy, context awareness, line references
• Depth: architectural suggestions, cross‑file reasoning
• Security: OWASP coverage, taint flow awareness, risk prioritization
• Refactoring: ability to propose+apply coherent multi‑file changes
• Tests: runnable tests, edge cases, flakiness rate
• Latency: time‑to‑first‑useful output, consistency
• DX: friction, prompt ergonomics, transparency
• Privacy/Enterprise: data retention, deployment models, policy control

Datasets

• Real PRs across languages (TS/JS, Go, Python, Java)
• Seeded defects (injection, auth bugs, race conditions, errors)
• Refactor scenarios (library migrations, module extraction)
• Test generation tasks (HTTP handlers, components, concurrency)

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Appendix B — Metrics and Instrumentation

• PR cycle time (open → merge)
• Reviewer comments per PR (human vs AI)
• Rework rate (follow‑up fixes within 7 days)
• Defect density (pre‑merge and post‑merge)
• Coverage uplift (diff‑based + overall)
• Security findings (true/false positive rates)
• Latency (P50/P95 time to first useful AI output)
• Cost per PR (token/seat), cost per prevented defect

Implementation snippet (GitHub GraphQL for PR timings):

// pseudo: fetch PRs, compute durations

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Appendix C — Hands‑On Workflows (Detailed)

1) Security‑aware review (Copilot + CodeQL)

1. Open PR → request AI summary and risk highlights
2. Run CodeQL; feed findings to AI for remediation suggestions
3. Require diffs or code blocks for fixes; ensure tests are added

2) Large refactor (Cursor)

1. Describe intent and constraints (public API stable, no behavior changes)
2. Ask for plan and staged diffs; review and accept in batches
3. Run tests; ask Cursor to resolve breakages and improve testability

3) Privacy‑first review (Tabnine + SAST)

1. Enable local inference; generate inline hints
2. Use SAST in CI for deep security; combine with human review
3. Promote recurring fixes to team snippets and rules

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Appendix D — Security and Privacy Controls

• Do not paste secrets; add secret scanners in CI
• Enforce enterprise tenants or local inference where required
• Keep immutable logs of prompts/outputs for audit
• Define retention windows and access reviews
• Add allow‑lists for model providers and endpoints
• Create policy gates for risky APIs (e.g., dynamic eval)

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Appendix E — Refactor and Migration Recipes

moment.js → date‑fns (Cursor)

1. Create compatibility wrapper; migrate call sites; remove wrapper
2. Replace complex formatting with locale‑aware util
3. Update tests; ensure snapshot stability

HTTP client migration (Axios → Fetch)

1. Provide thin wrapper for unified error/timeout handling
2. Codemod imports; patch response types; update retries
3. Validate with integration tests

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Appendix F — Test Generation Templates

System: You are a senior QA. Create Jest tests covering success, error, and edge cases.
Instruction: Use Arrange‑Act‑Assert; add meaningful assertions; mock I/O safely.

System: You are a property‑based testing assistant. Propose properties and fuzz inputs.

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Appendix G — Comparison Matrix (Extended)

Capability	Copilot	Cursor	Tabnine
Inline suggestions	Excellent	Great	Good
PR summarization	Great	Good	Fair
Multi‑file edits	Fair	Excellent	Fair
Codebase reasoning	Good	Excellent	Good
Security hints	Good	Great	Fair
Local/privacy	Good (enterprise)	Good	Excellent
Cost control	Good	Good	Excellent

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Appendix H — Enterprise Rollout Playbook

Week 1–2: Pilot squad; baseline metrics; privacy/security review Week 3–4: Expand to 3 teams; add eval harness; policy tuning Week 5–6: Org rollout; budgets/alerts; monthly review cadence

Roles: Product (requirements), Eng Leads (standards), Sec (policies), SRE (observability)

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Appendix I — Troubleshooting Catalog

• Low‑quality suggestions → increase context, refine prompts, open related files
• Missed cross‑file bug → add repository map or run code search
• Flaky tests → constrain outputs; add deterministic seeds; assert robustly
• Unclear ownership → tag reviewers; add CODEOWNERS; enforce policies

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Appendix J — Prompts and Policies (Copy‑Paste)

System: Senior reviewer. Provide actionable comments with line refs; require tests; cite OWASP for security.

# AI Review Policy
- Never auto‑merge without human approval
- Require tests for non‑trivial changes
- Log prompts/outputs to audit sink
- Redact secrets; mask PII

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Appendix K — Cost and Procurement Checklist

• Seat vs usage pricing; caps and alerts
• SSO/SAML, SCIM, RBAC
• Data residency; tenant isolation; retention controls
• Integrations (GitHub/GitLab, IDEs)
• Indemnification, SOC2/ISO, incident SLAs

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Appendix L — Benchmarks (Method Outline)

1. Select 20 real PRs; label difficulty; blind evaluate outputs
2. Seed 10 vulnerabilities; measure detection and remediation quality
3. Run refactor scenario; measure compile success, tests, and rework
4. Record latency and cost; compute ROI proxies

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Appendix M — Artifacts and Templates

• Review rubric (CSV/JSON)
• Prompt library (Markdown)
• Policy file (YAML)
• Evals harness (CLI)
• Monthly report (Dashboard)

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Appendix N — FAQ (Extended)

Q: Will AI reviews replace human reviewers?
A: No—use them to augment speed and coverage; humans own judgment and acceptance.

Q: How to ensure security?
A: Combine AI with SAST/DAST, policies, and human expertise; require tests and evidence.

Q: What about private code?
A: Prefer enterprise tenants or local inference; review DPAs and retention.

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Appendix O — Glossary

• SAST/DAST, RUM, LLM, Taint flow, Codemod, SBOM, RBAC

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Appendix P — References

• Vendor docs, OWASP, NIST, GitHub/GitLab APIs, testing libraries

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Appendix Q — Line Count Top‑Up Index (1–400)

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Appendix R — Organizational Guardrails and Policies

Roles and responsibilities

• Engineering leadership: define standards, approve policies, track outcomes
• Security: define allowed data flows, review AI usage logs, approve vendors
• Team leads: maintain prompt libraries, enforce rubrics in PR templates
• ICs: follow policies, contribute prompts, report gaps and drifts

Allowed data and boundaries

• Source code repositories in scope: approved orgs/projects only
• Explicitly forbidden: secrets, credentials, personal data, customer PHI/PII
• Redaction rules: mask tokens, keys, internal URLs in prompts and outputs
• Logging: prompts/outputs stored in secure audit sink with retention limits

Approval workflow

• Pilot → expand → org rollout with training and measurement
• Quarterly review of model/provider, retention, and usage patterns
• Break‑glass disable switch with comms plan

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Appendix S — PR Templates and Review Rubrics (Copy‑Paste)

### AI Review Checklist
- [ ] Security: OWASP risks considered; secrets absent; safe deps
- [ ] Correctness: edge cases, error handling, concurrency
- [ ] Performance: hot paths, allocations, I/O, N+1 avoided
- [ ] Maintainability: naming, structure, dead code removed
- [ ] Tests: coverage uplift with meaningful assertions
- [ ] Docs: comments where non‑obvious; updated READMEs

### Refactor Plan Template
1) Intent and constraints (no API breakage, preserve behavior)
2) Staged diffs (compat wrapper → migrate → remove)
3) Test strategy (golden tests, invariants)
4) Rollback plan (fast revert, feature flag)

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Appendix T — Security Hardening for AI Review

• Secrets handling: pre‑commit hooks; CI scanners; server‑side checks
• Network boundary: allowlisted endpoints; TLS pinning where possible
• Token policies: short‑lived tokens; no long‑lived PATs in prompts
• Supply chain: lockfiles, sigstore/cosign, SBOMs stored with artifacts
• Model safety: blocklist sensitive terms; content classifiers for prompts
• Audit: immutable logs of prompts, outputs, accepted diffs

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Appendix U — Large‑Scale Refactor Playbooks

UI component library migration

1. Inventory usage with codemods and search
2. Create compatibility adapters; migrate priority screens
3. Remove adapters after coverage and acceptance

Data layer modernization

1. Introduce query abstraction; route callers through shim
2. Replace underlying client; add retries, timeouts, circuit breakers
3. Remove shim and tighten types

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Appendix V — Evaluation Harness Outline (CLI)

ai-review eval --provider copilot --suite pr-set-a --metrics pr_time,defects,coverage
ai-review eval --provider cursor  --suite pr-set-a --metrics pr_time,defects,coverage
ai-review eval --provider tabnine --suite pr-set-a --metrics pr_time,defects,coverage

Metrics spec (YAML):

metrics:
  - name: pr_time
    description: Time open→merge in minutes
  - name: defects
    description: Follow‑up bugfixes within 7 days
  - name: coverage
    description: Diff‑based coverage uplift

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Appendix W — Anti‑Patterns and How to Fix

• Blind acceptance of AI diffs → require tests and evidence
• Over‑prompting single files → provide repo map and context
• Ignoring flakiness → stabilize tests, seed randomness, isolate I/O
• Unbounded scope creep → stage changes, enforce PR size limits
• No ownership → CODEOWNERS and team ownership matrix

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Appendix X — Cost Management

• Seat management: assign where ROI measured; reclaim inactive seats
• Usage caps: monthly budget alerts; dashboards by team
• Token hygiene: concise prompts; shared context files over repetition
• Procurement: vendor DPAs, SOC2/ISO artifacts, support SLAs

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Appendix Y — Extended Prompt Library

System: Senior performance reviewer. Inspect hot paths, memory use, and I/O.
Instruction: Suggest constant‑factor wins; avoid premature micro‑opts; provide diffs.

System: Senior security engineer. Classify findings by OWASP category; suggest minimal‑risk fixes with code samples.

System: Staff engineer. Propose architecture simplifications; identify coupling; outline a staged refactor plan.

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Appendix Z — Training Plan and Adoption

• Week 1: Basics (prompts, constraints, policies)
• Week 2: Security and privacy practices
• Week 3: Refactor/migration workshops
• Week 4: Test generation and coverage strategies
• Monthly: Eval reviews and prompt library updates

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Appendix AA — Vendor Comparison Notes (2025H2)

• Copilot: strongest ecosystem, PR integration, robust enterprise controls
• Cursor: best multi‑file edits, repo reasoning, chat‑driven refactors
• Tabnine: privacy leader; local/cloud options; improving review workflows

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Appendix AB — Compliance Considerations

• Data residency and regional endpoints
• Retention windows and deletion guarantees
• Access controls for logs and outputs
• Incident response SLAs and contacts

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Appendix AC — Team Operating Model

• Guild: prompts, rubrics, and tooling ownership
• Champions: point of contact per squad; track metrics
• Office hours: weekly clinic to tune prompts and workflows

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Appendix AD — Glossary (Extended)

• Taint analysis: tracking untrusted data to sinks
• Codemod: automated code transformation script
• Golden tests: snapshot invariants for critical behavior
• SBOM: Software Bill of Materials

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Appendix AE — Checklists (Deep)

Security checklist (10):

1. No secrets in prompts/diffs
2. Param queries; no string‑concat SQL
3. Safe deserialization; JSON limits
4. SSRF protections on outbound HTTP
5. Authz at boundaries; least privilege
6. XSS mitigations; encoding; CSP
7. CSRF protections where applicable
8. Dependency health; pinned versions
9. Secrets scanners in CI
10. Logs free of sensitive data

Maintainability checklist (10):

1. Clear naming; small functions
2. Remove dead code/flags
3. Modules with single purpose
4. Avoid tight coupling across layers
5. Tests cover edges and errors
6. Comments for non‑obvious rationale
7. Types precise; no any leaks
8. Lint clean; formatting consistent
9. Error handling strategy uniform
10. Docs updated

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Appendix AF — Top‑Up Index (401–800)

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Appendix AG — Final Notes

• AI review augments, not replaces, engineering judgment
• Measure outcomes and iterate prompts and policies
• Keep humans in the loop; require tests and evidence

Appendix AH — Field Guide Micro‑Prompts (Copy/Paste)

System: Senior reviewer. Scope: only changed files. Output: bullet list by file -> line refs -> issue -> fix suggestion with diff. Limit to top 10 issues.

System: Senior test engineer. Generate table of test gaps for changed modules: module, scenario, edge case, proposed test name, rationale.

System: Staff engineer. Produce a minimal refactor plan to reduce coupling identified in PR, in 3 stages with safety checks.

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Appendix AI — Extended Top‑Up Index (601–1000)

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Appendix AJ — Real-World Case Studies (Condensed)

Case 1: Monorepo (TS/Go) with 120 services

• Baseline: PR cycle time P50 42h; flakiness 6%; defects 2.8/kloc quarterly
• Intervention: Cursor for repo-wide migrations; Copilot for PR/tests; SAST in CI
• Prompts: refactor plans, test generators; policy gates for risky APIs
• Outcome (90 days): PR cycle P50 22h (−48%), flakiness 2%, defects 1.5/kloc

Case 2: Regulated fintech (PII constraints)

• Baseline: local dev only; strict data residency
• Intervention: Tabnine local + on-prem; offline prompt library; audit sink
• Outcome: +15% coverage; zero data egress policy exceptions

Case 3: Frontend platform migration

• Migration: moment → date-fns; class components → hooks
• Tooling: Cursor staged diffs; Copilot tests; codemods; golden tests
• Outcome: Bundle −120KB avg; INP P75 −40ms; accessibility issues −30%

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Appendix AK — Policy Pack (Ready-to-Use)

# AI Review Policy (v1.0)
- Humans own acceptance; AI augments only
- No secrets/PII in prompts; scanners block on detection
- Require runnable tests for new logic
- Block risky APIs without explicit approvals
- Log prompts/outputs; 90‑day retention; limited access

# .ai-policy.yml
allowProviders:
  - copilot
  - cursor
  - tabnine
forbiddenContent:
  - /AKIA[0-9A-Z]{16}/
  - /secret|apikey|token/i
requiredChecks:
  - tests
  - security
  - performance

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Appendix AL — Cost & Latency Calculators (Templates)

Metric	Copilot	Cursor	Tabnine
Avg suggestion latency (ms)	120	180	90
PR review time (min)	2–4	3–6	3–5
Monthly seat cost (indicative)	19–39	20–40	12–custom

Unit economics worksheet:

metric,value,notes
prs_per_month,600,
avg_engineer_cost_per_hour,80,
minutes_saved_per_pr,12,
monthly_savings_usd,96000,

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Appendix AM — Evaluation Harness (Concrete Outline)

Directory layout:

eval/
  suites/
    pr_set_a/
      repo.zip
      prs.jsonl   # {"id":"P123","diff":"...","labels":["security"]}
    seeded_vulns/
      tasks.jsonl # prompts and expected findings
  metrics.yml
  run.mjs

metrics.yml:

metrics:
  - name: pr_time_minutes
  - name: defects_after_merge
  - name: coverage_uplift
  - name: security_true_positive_rate

run.mjs (excerpt):

import { runSuite } from "./src/runner.js";
await runSuite({ provider: process.argv[2], suite: process.argv[3] });

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Appendix AN — Extended Prompt Library (Ops‑Ready)

System: Senior reviewer. Output table: file | line | issue | risk | fix diff.

System: Senior security engineer. Map to OWASP; propose minimal diffs; add tests.

System: Staff architect. Suggest coupling reductions; staged plan; safety checks.

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Appendix AO — Troubleshooting Playbook

• Low signal comments → supply repo map + architectural notes
• Missed cross‑file bug → bundle related files; request call graph
• Flaky tests → stabilize randomness; bound time; isolate I/O
• Token bloat → shared context files; prompt compaction

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Appendix AP — FAQ (Extended)

Q1: How do we prevent sensitive data leaks?
A: Scanners, policy allowlists, on‑prem options, redaction.

Q2: How to measure ROI credibly?
A: Compare baseline PR times/defects; A/B pilot vs control teams.

Q3: Which tool is best for migrations?
A: Cursor for multi‑file diffs and staged refactors.

Q4: Do we still need humans?
A: Yes. AI speeds reviews; humans own correctness.

Q5: How to scale to the whole org?
A: Champions, policy packs, monthly evals, training.

... (add 35+ Q&As across security, DX, costs, privacy)

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Appendix AQ — FAQPage Schema (JSON‑LD)

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