# [Epic 001] AI-Assisted Code Review Optimization System (Release #TBD) **Overview**: Implement tooling and workflows to optimize human review capacity for AI-generated code through automated review guides, structured PR templates, incremental integration patterns, and trust metrics. **Key Goals**: - Generate AI-powered review guides that summarize PR changes and highlight areas requiring human attention - Standardize PR templates with reviewer guidance, testing evidence, and rollback plans - Create workflow tooling for breaking large AI-generated features into reviewable chunks - Implement trust metrics tracking defect rates, time-to-production, and rollback frequency for AI-generated code - Reduce average PR review time by 40% while maintaining or improving code quality ## Child Issues ### Phase 1: Review Guide Automation - #001 [1 of 7] AI Review Guide Generator 🟢 [S/Low] (Epic #001) - #002 [2 of 7] Review Guide Integration with CI/CD 🟡 [M/Medium] (Epic #001) ### Phase 2: Structured PR Templates - #003 [3 of 7] Enhanced PR Template System 🟢 [S/Low] (Epic #001) - #004 [4 of 7] PR Template Enforcement and Validation 🟡 [M/Medium] (Epic #001) ### Phase 3: Incremental Integration Tooling - #005 [5 of 7] PR Splitter Tool for Large Changes 🟠 [L/High] (Epic #001) ### Phase 4: Trust Metrics & Monitoring - #006 [6 of 7] AI Code Trust Metrics Dashboard 🟡 [L/Medium] (Epic #001) - #007 [7 of 7] Automated Trust-Based Review Routing 🟠 [M/High] (Epic #001) ## Dependencies - **Depends on:** Existing automated checks (linting, testing, security scanning) - **Blocks:** None ## Definition of Done - [ ] All child issues completed and merged - [ ] 40% reduction in average PR review time achieved - [ ] Trust metrics collecting data for 30+ days - [ ] Team training completed on new workflows - [ ] Documentation published for all new tools and processes --- **Related Spec:** review-optimization-system-spec.md --- # Child Issue Details ## [1 of 7] AI Review Guide Generator 🟢 [S/Low] (Epic #001) ### Overview & User Story As a code reviewer, I want an AI-generated summary of PR changes with highlighted areas of concern, so that I can quickly understand the scope and focus my review on high-risk areas without reading every line of code. ### Acceptance Criteria - Given a PR with code changes, when the AI analyzer runs, then it generates a structured review guide with change summary, risk assessment, and focus areas - Review guide includes file-by-file breakdown identifying boilerplate vs critical logic - High-risk changes (auth, data access, external APIs) are automatically flagged - Review guide estimates human review time based on change complexity - Guide includes links to relevant documentation and architectural decision records ### Technical Implementation - Create `tools/review-guide-generator.py` using OpenAI/Anthropic API for analysis - Implement change classification logic (boilerplate, business logic, infrastructure, etc.) - Add risk scoring algorithm based on file types, change size, and semantic analysis - Generate markdown output following standardized template - Create configuration file for customizing risk thresholds and focus areas - Add unit tests for classification and risk scoring logic ### Dependencies - GitHub API access for PR metadata - LLM API access (OpenAI GPT-4 or Anthropic Claude) - Existing git diff parsing utilities ### Estimated Effort **Classification:** 🟢 ai-solo (AI-autonomous, human reactive) --- ## [2 of 7] Review Guide Integration with CI/CD 🟡 [M/Medium] (Epic #001) ### Overview & User Story As a developer, I want review guides automatically generated and posted when I create a PR, so that reviewers immediately have context without waiting for manual summaries or separate tools. ### Acceptance Criteria - When a PR is created, review guide is automatically generated within 2 minutes - Guide is posted as the first comment on the PR with consistent formatting - Failed generation triggers notification without blocking PR workflow - Guide updates when significant new commits are pushed - System handles rate limits and API failures gracefully with retry logic ### Technical Implementation - Create GitHub Action workflow file `.github/workflows/review-guide.yml` - Implement webhook handler or use existing CI/CD triggers - Add error handling and retry logic with exponential backoff - Create notification system for generation failures (Slack/email) - Implement caching to avoid redundant LLM calls for unchanged diffs - Add monitoring and logging for generation success rates and timing ### Dependencies - #001 AI Review Guide Generator must be completed - GitHub Actions enabled on repository - Secrets configured for LLM API keys ### Estimated Effort **Classification:** 🟡 ai-led (AI-led, human validates) --- ## [3 of 7] Enhanced PR Template System 🟢 [S/Low] (Epic #001) ### Overview & User Story As a PR author, I want structured templates that guide me to provide testing evidence, reviewer guidance, and rollback plans, so that reviewers have all necessary context and my PRs are reviewed faster. ### Acceptance Criteria - PR template includes sections for change summary, testing evidence, reviewer focus areas, and rollback plan - Template provides examples and guidance for each section - Different templates available for features, bugs, and refactoring work - Template checklist ensures all required sections are addressed - Template supports embedding screenshots, test output, and demo videos ### Technical Implementation - Create `.github/PULL_REQUEST_TEMPLATE/feature.md` for feature PRs - Create `.github/PULL_REQUEST_TEMPLATE/bugfix.md` for bug fixes - Create `.github/PULL_REQUEST_TEMPLATE/refactor.md` for refactoring - Add markdown guidance and examples within each template - Create documentation in `docs/pr-guidelines.md` with examples of good PRs - Add template selector in GitHub PR creation flow ### Dependencies - None ### Estimated Effort **Classification:** 🟢 ai-solo (AI-autonomous, human reactive) --- ## [4 of 7] PR Template Enforcement and Validation 🟡 [M/Medium] (Epic #001) ### Overview & User Story As a team lead, I want automated validation that PRs include required information like testing evidence and reviewer guidance, so that incomplete PRs are caught early and review quality remains consistent. ### Acceptance Criteria - Bot checks PR description against template requirements when PR is opened - Missing required sections trigger a status check failure with helpful message - Bot provides inline suggestions for improving PR descriptions - Team can configure which sections are required vs optional per project - Validation re-runs when PR description is edited ### Technical Implementation - Create GitHub Action in `.github/workflows/pr-validation.yml` - Implement PR description parser that extracts and validates sections - Add configurable validation rules in `.github/pr-validation-config.yml` - Create status check that blocks merge when validation fails (optional config) - Implement bot that posts helpful comments with section examples - Add analytics logging for validation failure patterns ### Dependencies - #003 Enhanced PR Template System must be completed - GitHub Actions bot permissions configured ### Estimated Effort **Classification:** 🟡 ai-led (AI-led, human validates) --- ## [5 of 7] PR Splitter Tool for Large Changes 🟠 [L/High] (Epic #001) ### Overview & User Story As a developer working with AI-generated features, I want tooling to intelligently split large changesets into logical, reviewable PRs while maintaining dependency order, so that I can ship complete features through incremental, reviewable chunks. ### Acceptance Criteria - Tool analyzes git diff and suggests logical split points (models, logic, API, UI, tests) - Generates separate branches for each logical chunk with correct dependency order - Creates draft PRs with descriptions explaining the split and dependencies - Validates that all chunks together equal the original changeset - Supports feature flags for atomic feature deployment despite incremental merging ### Technical Implementation - Create CLI tool `tools/pr-splitter.py` with git and GitHub API integration - Implement AST-based code analysis to identify dependencies between changes - Build change classification system (schema, business logic, API, UI, tests) - Create branch management logic for generating ordered dependent branches - Implement PR creation with cross-linking and dependency documentation - Add validation mode that ensures all changes are included in split PRs ### Dependencies - Git command-line tools - GitHub API access with PR creation permissions - Tree-sitter or similar AST parser for dependency analysis ### Estimated Effort **Classification:** 🟠 ai-assisted (Human-led, AI assists) --- ## [6 of 7] AI Code Trust Metrics Dashboard 🟡 [L/Medium] (Epic #001) ### Overview & User Story As an engineering manager, I want visibility into defect rates, time-to-production, and rollback frequency for AI-generated code by type and complexity, so that I can make data-driven decisions about review requirements and identify areas where AI excels. ### Acceptance Criteria - Dashboard displays defect rate by PR classification (AI-solo, AI-led, AI-assisted, AI-limited) - Metrics include time-to-merge, time-to-production, and rollback frequency - Charts show trends over time with week-over-week comparisons - Drill-down capability to see specific PRs contributing to metrics - Export functionality for metrics data in CSV format ### Technical Implementation - Create data collection script that tags PRs with AI classification labels - Implement metrics aggregation pipeline pulling from GitHub, CI/CD, and incident tracking - Build dashboard using Grafana, Metabase, or custom web app - Create database schema for storing historical metrics data - Add automated defect detection by tracking bug fix PRs that reference original features - Implement alert system for metrics falling outside acceptable thresholds ### Dependencies - GitHub API for PR and commit data - CI/CD system for deployment tracking - Incident tracking system (Jira, Linear, etc.) for rollback data ### Estimated Effort **Classification:** 🟡 ai-led (AI-led, human validates) --- ## [7 of 7] Automated Trust-Based Review Routing 🟠 [M/High] (Epic #001) ### Overview & User Story As a team lead, I want PRs automatically assigned to appropriate reviewers with reduced requirements for proven-reliable AI-generated code patterns, so that senior reviewers focus on high-risk changes while routine AI code flows through lighter review. ### Acceptance Criteria - System assigns reviewers based on code area expertise and current workload - Low-risk AI-generated code with proven track record requires single junior reviewer - High-risk changes automatically require senior reviewer and potentially multiple reviewers - Review requirements adjust dynamically based on historical trust metrics - Team can override automatic routing with manual reviewer assignment ### Technical Implementation - Create GitHub Action for automated reviewer assignment `.github/workflows/assign-reviewers.yml` - Implement risk scoring algorithm combining change type, file paths, and historical data - Build reviewer expertise mapping based on past PR reviews and CODEOWNERS - Add workload balancing logic tracking current assigned reviews per person - Create escalation rules for high-risk changes requiring multiple approvals - Implement override mechanism preserving manual assignments ### Dependencies - #006 AI Code Trust Metrics Dashboard providing historical reliability data - CODEOWNERS file or team expertise mapping - GitHub API with reviewer assignment permissions ### Estimated Effort **Classification:** 🟠 ai-assisted (Human-led, AI assists) --- **Related Spec:** review-optimization-system-spec.md