ripple-ai-detector

# Ripple User Stories & Acceptance Criteria ## Persona Definitions ### Primary Persona: Alex (Senior Developer) - **Role**: Senior Full-Stack Developer at a mid-size tech company - **Experience**: 8+ years, heavy AI assistant user (Claude, Cursor, GitHub Copilot) - **Pain Point**: Recently shipped 3 bugs caused by AI-generated code that broke production - **Motivation**: Wants to use AI confidently without risking team reputation - **Tech Setup**: VS Code, Node.js/TypeScript, Git workflows, team code reviews ### Secondary Persona: Sam (Engineering Manager) - **Role**: Engineering Manager of 8-person team - **Experience**: 12+ years, manages team using AI tools - **Pain Point**: Team velocity increased with AI, but bug rate also increased - **Motivation**: Wants team productivity gains without quality regression - **Tech Setup**: GitHub/GitLab, CI/CD pipelines, team dashboards ## Epic 1: Core CLI Validation ### Story 1.1: Quick Code Safety Check **As Alex**, I want to quickly validate my staged changes before committing, so that I can catch AI-generated bugs before my team sees them. **Acceptance Criteria:** - [ ] Can run `ripple validate` from any git repository - [ ] Analysis completes in under 2 seconds for typical changes (1-5 files) - [ ] Shows clear pass/fail result with confidence score - [ ] Identifies specific issues with file names and line numbers - [ ] Works offline (no internet required for core analysis) **Definition of Done:** - [ ] CLI command works on macOS, Windows, Linux - [ ] Handles JavaScript and TypeScript files - [ ] Provides actionable error messages - [ ] Unit tests cover 90%+ of validation logic - [ ] Documentation includes usage examples --- ### Story 1.2: AI Change Detection **As Alex**, I want the tool to detect when my changes were likely generated by AI, so that I can be extra careful with those modifications. **Acceptance Criteria:** - [ ] Detects AI-generated changes with 70%+ accuracy - [ ] Shows confidence percentage for AI detection - [ ] Explains why it thinks changes are AI-generated - [ ] Flags multi-file changes as high-risk - [ ] Identifies function signature changes without caller updates **Definition of Done:** - [ ] Tested against known AI-generated code samples - [ ] False positive rate under 15% - [ ] Provides clear reasoning for AI detection - [ ] Performance impact under 200ms additional analysis time --- ### Story 1.3: Git Integration **As Alex**, I want the tool to automatically check my code before I commit, so that I never accidentally commit breaking changes. **Acceptance Criteria:** - [ ] Can install git pre-commit hook with `ripple hook install` - [ ] Hook prevents commits when errors are found - [ ] Hook allows commits when only warnings are present (with confirmation) - [ ] Can bypass hook with `--no-verify` flag - [ ] Hook works with existing git workflow tools **Definition of Done:** - [ ] Hook installation works across different git setups - [ ] Hook removal is clean and complete - [ ] Performance acceptable for daily development workflow - [ ] Integrates with existing pre-commit frameworks --- ## Epic 2: Advanced Analysis Features ### Story 2.1: Function Signature Change Detection **As Alex**, I want to be warned when I change a function signature without updating all the places that call it, so that I don't break other parts of the codebase. **Acceptance Criteria:** - [ ] Detects function parameter additions, removals, type changes - [ ] Finds all call sites that need updating - [ ] Works across multiple files and modules - [ ] Handles TypeScript interfaces and type definitions - [ ] Provides suggested fixes when possible **Definition of Done:** - [ ] 85%+ accuracy on function signature detection - [ ] Handles complex TypeScript scenarios - [ ] Performance scales to large codebases (100+ files) - [ ] Clear error messages with fix suggestions --- ### Story 2.2: Import/Export Validation **As Alex**, I want to catch import/export mismatches immediately, so that I don't waste time debugging module resolution errors. **Acceptance Criteria:** - [ ] Validates named imports match exports - [ ] Checks default import/export consistency - [ ] Handles re-exports and barrel files - [ ] Works with relative and absolute import paths - [ ] Detects unused imports after refactoring **Definition of Done:** - [ ] Covers ES6 modules, CommonJS, and TypeScript - [ ] Handles complex module resolution scenarios - [ ] Performance optimized for large dependency trees - [ ] Integration with TypeScript compiler API --- ## Epic 3: VS Code Extension ### Story 3.1: One-Click Safety Check **As Alex**, I want a button in VS Code to quickly check my current changes, so that I can validate my work without switching to the terminal. **Acceptance Criteria:** - [ ] "AI Safety Check" button visible in editor toolbar - [ ] Command available in Command Palette (Ctrl+Shift+P) - [ ] Keyboard shortcut (Ctrl+Shift+R) for quick access - [ ] Results display in VS Code output panel - [ ] Status bar shows current safety status **Definition of Done:** - [ ] Extension installs from VS Code Marketplace - [ ] UI follows VS Code design guidelines - [ ] Works with VS Code themes (light/dark) - [ ] Performance doesn't block editor responsiveness --- ### Story 3.2: Inline Problem Indicators **As Alex**, I want to see validation issues directly in my code editor, so that I can fix problems without switching between tools. **Acceptance Criteria:** - [ ] Red underlines for errors, yellow for warnings - [ ] Hover tooltips show detailed issue description - [ ] Problems appear in VS Code Problems panel - [ ] Click on problem navigates to exact location - [ ] Issues update in real-time as I edit **Definition of Done:** - [ ] Integrates with VS Code diagnostics API - [ ] Performance handles real-time updates - [ ] Clear, actionable error messages - [ ] Respects user's error/warning preferences --- ### Story 3.3: AI Detection Indicators **As Alex**, I want to see when VS Code thinks my changes are AI-generated, so that I can double-check those modifications more carefully. **Acceptance Criteria:** - [ ] Status bar shows "AI Code Detected" with confidence % - [ ] Different colors for different confidence levels - [ ] Tooltip explains why code is flagged as AI-generated - [ ] Option to dismiss AI detection for specific changes - [ ] Historical view of AI detection patterns **Definition of Done:** - [ ] Visual indicators are clear but not intrusive - [ ] Performance doesn't impact typing responsiveness - [ ] User can configure sensitivity levels - [ ] Integrates with existing VS Code workflow --- ## Epic 4: Team Features & Dashboard ### Story 4.1: Team Usage Overview **As Sam (Engineering Manager)**, I want to see how my team is using AI coding tools and what issues are being caught, so that I can understand our code quality trends. **Acceptance Criteria:** - [ ] Web dashboard shows team usage statistics - [ ] Charts of AI detection over time - [ ] List of most common issues found - [ ] Individual developer usage (anonymizable) - [ ] Export data for further analysis **Definition of Done:** - [ ] Dashboard loads in under 2 seconds - [ ] Data updates within 5 minutes of CLI usage - [ ] Responsive design works on mobile/tablet - [ ] Role-based access control --- ### Story 4.2: Team Configuration Management **As Sam**, I want to configure validation rules for my entire team, so that we have consistent code quality standards. **Acceptance Criteria:** - [ ] Web interface for editing team configuration - [ ] Push configuration updates to team members - [ ] Different rule sets for different projects - [ ] Version control for configuration changes - [ ] Team member notification of config changes **Definition of Done:** - [ ] Configuration sync works reliably - [ ] Team members can override specific rules locally - [ ] Audit trail for configuration changes - [ ] Backup/restore functionality --- ## Epic 5: Authentication & Billing ### Story 5.1: Easy License Setup **As Alex**, I want to easily authenticate with my license key, so that I can start using the tool immediately after purchase. **Acceptance Criteria:** - [ ] `ripple auth login <key>` command works reliably - [ ] License validation happens instantly (cached) - [ ] Clear error messages for invalid licenses - [ ] Automatic license renewal handling - [ ] Offline mode for authenticated users **Definition of Done:** - [ ] Authentication persists across CLI sessions - [ ] Secure storage of license credentials - [ ] Works behind corporate firewalls - [ ] Clear upgrade prompts for plan limits --- ### Story 5.2: Usage Tracking & Limits **As Alex**, I want to understand my usage limits and current consumption, so that I can manage my subscription effectively. **Acceptance Criteria:** - [ ] `ripple auth status` shows current usage - [ ] Warning when approaching plan limits - [ ] Clear explanation of what counts toward limits - [ ] Graceful degradation when limits exceeded - [ ] Easy upgrade path from CLI **Definition of Done:** - [ ] Usage tracking is accurate and real-time - [ ] No loss of functionality due to brief network outages - [ ] Clear documentation of billing policies - [ ] Integration with Stripe billing portal --- ## Epic 6: Performance & Reliability ### Story 6.1: Large Codebase Support **As Alex**, I want the tool to work quickly even on large codebases, so that it doesn't slow down my development workflow. **Acceptance Criteria:** - [ ] Analysis of 50+ files completes in under 5 seconds - [ ] Incremental analysis only processes changed files - [ ] Memory usage stays under 200MB for large projects - [ ] Progress indicator for long-running analysis - [ ] Graceful handling of timeout scenarios **Definition of Done:** - [ ] Performance benchmarks documented - [ ] Optimization for common project structures - [ ] Caching strategy reduces repeat analysis time - [ ] Works with monorepos and large React/Node projects --- ### Story 6.2: Robust Error Handling **As Alex**, I want clear error messages when something goes wrong, so that I can quickly resolve issues and get back to coding. **Acceptance Criteria:** - [ ] Parsing errors include file location and context - [ ] Network errors have clear retry instructions - [ ] Configuration errors suggest corrections - [ ] Crash recovery preserves user work - [ ] Debug mode provides detailed logging **Definition of Done:** - [ ] Error messages tested with non-technical users - [ ] Comprehensive error logging for support - [ ] Graceful degradation for partial failures - [ ] Recovery suggestions for common issues --- ## User Journey Maps ### Journey 1: First-Time Setup (Alex) 1. **Discovery**: Hears about Ripple from AI coding community 2. **Research**: Reads documentation, watches demo video 3. **Trial**: Downloads free version, tests on current project 4. **Value Recognition**: Catches AI-generated bug before commit 5. **Purchase**: Upgrades to paid plan 6. **Integration**: Installs VS Code extension, sets up git hooks 7. **Habit Formation**: Uses daily, becomes part of workflow ### Journey 2: Team Adoption (Sam) 1. **Problem Recognition**: Notices increased bugs from AI usage 2. **Evaluation**: Tests Ripple with small team subset 3. **Pilot**: Runs 30-day team pilot program 4. **Analysis**: Reviews dashboard data, measures impact 5. **Rollout**: Deploys to entire engineering team 6. **Optimization**: Adjusts rules, configures team standards 7. **Scaling**: Becomes standard part of development process --- ## Success Metrics by Story ### Phase 1 (MVP) - **Time to First Value**: User catches first issue within 5 minutes of installation - **Analysis Speed**: 95% of validations complete in under 2 seconds - **Accuracy**: 80% of users report issues found were actually problematic - **Adoption**: 70% of trial users install git hooks ### Phase 2 (Advanced Features) - **AI Detection Accuracy**: 70% precision, 60% recall on AI-generated changes - **Issue Resolution**: 90% of flagged issues provide actionable fix guidance - **VS Code Integration**: 80% of users prefer extension over CLI - **User Retention**: 80% monthly active user retention ### Phase 3 (Team Features) - **Team Adoption**: Average team size of 5+ developers - **Dashboard Usage**: 60% of team managers check dashboard weekly - **Configuration**: 80% of teams customize default rules - **Revenue**: $25K MRR by month 18 --- This comprehensive set of user stories provides Augment Code with clear development targets and measurable success criteria for each feature.