# Epic and Story Specification Patterns: Implementation Guide

## Quick Reference: Attribute Checklists

### Epic Specification Checklist

**Hypothesis Stage**:
- [ ] Problem statement articulated
- [ ] Target user/market defined
- [ ] Expected outcome described
- [ ] Success metrics identified (min 3)
- [ ] Baseline and target values specified
- [ ] Measurement method defined
- [ ] Timeline for evaluation set

**Business Case Stage**:
- [ ] Cost estimation provided (development, operational, infrastructure)
- [ ] Expected benefits quantified (revenue, cost savings, retention impact)
- [ ] Duration estimates (exploration, analysis, implementation)
- [ ] MVP scope defined
- [ ] Risks identified and mitigation planned (min 3 risks)
- [ ] ROI threshold established
- [ ] Go/no-go decision criteria documented

**Portfolio Kanban Stage**:
- [ ] Epic Owner assigned
- [ ] Status in correct Kanban stage
- [ ] WIP limits respected
- [ ] Next stage entry criteria verified
- [ ] Governance review scheduled

---

### User Story Specification Checklist

**Story Foundation**:
- [ ] Title follows pattern: "As [persona], I want [action], so that [benefit]"
- [ ] User persona specified
- [ ] Business value articulated
- [ ] Story links to feature and epic
- [ ] Dependencies identified

**INVEST Compliance** (min score: 24/30):
- [ ] **Independent**: Identified and documented dependencies; score ≥4/5
- [ ] **Negotiable**: Acceptance criteria allow team discussion; score ≥4/5
- [ ] **Valuable**: Clear user/business benefit stated; score ≥4/5
- [ ] **Estimable**: Team confident in estimate; score ≥4/5
- [ ] **Small**: Fits in sprint; ≤8 story points; score ≥4/5
- [ ] **Testable**: All criteria measurable; score ≥4/5

**Acceptance Criteria**:
- [ ] Use Given-When-Then format (preferred) OR rule-based format
- [ ] Min 1, max 8 criteria
- [ ] All criteria are measurable/testable
- [ ] Cover happy path (min), alternatives (if applicable), error cases
- [ ] Include Scenario Outlines for data-driven testing (if applicable)

**Estimation**:
- [ ] Story points assigned (Fibonacci scale)
- [ ] Estimation confidence: High/Medium/Low
- [ ] Planning poker session completed
- [ ] Estimates recorded with assumptions

**Definition of Done Readiness**:
- [ ] All organizational DoD items applicable
- [ ] Team DoD items identified
- [ ] Success criteria testable/verifiable

**Dependencies & Risk**:
- [ ] Blocking dependencies identified
- [ ] Dependent stories identified
- [ ] Critical path analysis done
- [ ] Risk mitigation strategies documented

---

## Implementation Examples

### Example 1: Epic - Mobile Commerce Platform

```yaml
Epic:
  id: EPIC-2045
  name: "Mobile Commerce Platform v1.0"

  # Hypothesis Statement
  hypothesis_statement:
    problem: "45% of website traffic is mobile, but only 12% complete purchases on mobile devices"
    target_market: "Mobile-first consumers aged 18-35"
    expected_outcome: "Users can browse, add to cart, checkout, and track orders seamlessly on mobile"
    business_rationale: "Significant untapped revenue opportunity in mobile segment"

  # Success Metrics
  success_metrics:
    - metric: "Mobile conversion rate"
      baseline: 0.12
      target: 0.25
      unit: "percentage"
      timeline: "6 months post-launch"

    - metric: "Mobile revenue contribution"
      baseline: 18
      target: 35
      unit: "percentage of total revenue"
      timeline: "6 months post-launch"

    - metric: "App downloads"
      baseline: 0
      target: 100000
      unit: "downloads"
      timeline: "12 months"

    - metric: "Mobile daily active users"
      baseline: 0
      target: 50000
      unit: "users"
      timeline: "6 months"

  # Lean Business Case
  lean_business_case:
    estimated_cost:
      development_effort: 500  # story points
      development_cost: 250000  # $250k
      infrastructure_cost: 50000  # annual
      operational_cost: 30000  # annual

    expected_benefits:
      annual_recurring_revenue: 2500000  # $2.5M additional ARR
      cost_savings: 0
      market_expansion_value: 1000000  # $1M from new customer segment

    duration:
      exploration_weeks: 2
      analysis_weeks: 3
      implementation_weeks: 16
      total: 21  # ~5 months

    mvp_definition:
      scope: |
        - Product catalog browsing and search
        - Shopping cart functionality
        - Secure checkout with payment integration
        - Order tracking
        - Basic account management
      hypothesis: "Users can complete purchase flow 80% faster on mobile vs web"
      learning_goals:
        - "Determine mobile UX patterns preferred by target segment"
        - "Validate payment method preferences for mobile"
        - "Measure impact on conversion rates"

    risks:
      - type: "technical"
        description: "Payment processor API integration complexity"
        probability: "medium"
        impact: "high"
        mitigation: "Early spike on payment integration; select proven provider"

      - type: "market"
        description: "User adoption slower than expected"
        probability: "medium"
        impact: "medium"
        mitigation: "Launch with beta program; early user feedback loop"

      - type: "organizational"
        description: "Team lacks mobile development expertise"
        probability: "high"
        impact: "high"
        mitigation: "Hire mobile engineer; training program for team"

    decision_criteria:
      roi_threshold: 100  # Minimum 100% ROI
      payback_period_months: 12
      strategic_alignment_score: 9  # out of 10
      go_decision: true
      decision_date: "2025-02-15"

  # WSJF Priority
  wsjf:
    business_value: 21  # Highest: new major revenue stream
    time_criticality: 13  # High: competitive window closing
    risk_reduction: 8  # Medium-high: enables future features, reduces technical debt
    cost_of_delay: 42  # Sum of three components
    job_size: 21  # Large: 500 story points
    wsjf_score: 2.0  # 42/21 = 2.0; very high priority
    priority_rank: 1  # First to implement

  # Portfolio Status
  kanban_status: "Implementing"
  epic_owner: "Sarah Chen"
  target_completion_quarter: "Q2 2026"
```

---

### Example 2: Feature - Product Search & Filtering

```yaml
Feature:
  id: FEAT-2084
  parent_epic: "EPIC-2045"
  name: "Product Search & Advanced Filtering"

  description: |
    Enable users to efficiently find products through keyword search,
    category navigation, and advanced filtering options (price, rating,
    color, size, brand). Support for saved searches and search history.

  # Decomposition into stories
  stories:

    # Story 1: Happy Path Search
    - id: STORY-2451
      title: "User can search products by keyword"
      format: "As a shopper, I want to search products by entering keywords, so that I can quickly find what I'm looking for"

      user_persona: "Sarah, casual online shopper"
      estimated_points: 5

      acceptance_criteria:
        - given: "User is on the product search page"
          when: "User enters 'blue shirt' and clicks search"
          then: |
            - Search returns products matching keyword in title or description
            - Results displayed with product image, title, price, rating
            - Results paginated (20 per page)
            - Search takes <500ms

        - given: "User types search query"
          when: "Search query is typed in search box"
          then: |
            - Autocomplete suggestions appear after 200ms
            - Suggestions include popular searches
            - Suggestions filtered to match user input

        - scenario_name: "Empty search results"
          given: "User searches for non-existent product"
          when: "User submits search"
          then: |
            - User sees message "No products found for [query]"
            - User sees suggestions to refine search
            - User sees related categories to browse

      invest_score: 27/30  # Very good; ready for sprint
      invest_breakdown:
        independent: 5  # No dependencies; pure search feature
        negotiable: 4  # Small negotiation on UI layout possible
        valuable: 5  # Core user need
        estimable: 5  # Team confident; similar features done before
        small: 5  # Well-scoped; fits easily in sprint
        testable: 5  # All criteria measurable and automatable

      dependencies: []  # No story dependencies

      definition_of_done:
        organizational:
          - "Code reviewed and merged"
          - "Unit tests: >70% coverage"
          - "Integration tests: search API tested"
          - "Performance test: <500ms response"
          - "Deployed to staging"
        team_specific:
          - "Frontend tested on iPhone 12 and Galaxy S21 (team mobile standards)"
          - "Search endpoint tested with 100k+ products in DB"
          - "Autocomplete tested with 500+ suggestions"

      business_value:
        value_points: 8  # High value; critical search capability
        roi_percentage: 160  # 8 value points / 5 story points
        value_delivery: "ramp_up"  # Value grows as feature adoption increases

      splitting_options:
        # This story could be split using SPIDR
        spike: "Research search engine options (Elasticsearch, PostgreSQL FTS)"
        paths: "Basic text search vs. autocomplete"
        interface: "Desktop search vs. mobile touch-optimized search"
        data: "Search in 100 products vs. 100k products"
        rules: "Simple keyword match vs. relevance-based ranking"

    # Story 2: Filtering by Category
    - id: STORY-2452
      title: "User can filter search results by product category"
      estimated_points: 3
      dependencies:
        - story_id: "STORY-2451"
          type: "finish_to_start"
          reason: "Filtering builds on search results; search must be implemented first"

      acceptance_criteria:
        - given: "User has search results displayed"
          when: "User clicks category filter 'Shirts'"
          then: |
            - Results filtered to show only Shirts
            - Filter shows count: 'Shirts (1,234)'
            - Other categories updated: 'Pants (567)', 'Shoes (234)'
            - Filter can be cleared with 'Clear filters' button

    # Story 3: Advanced Filters (Price, Rating, etc.)
    - id: STORY-2453
      title: "User can filter by price range and minimum rating"
      estimated_points: 5
      dependencies:
        - story_id: "STORY-2451"
          type: "finish_to_start"

    # Story 4: Saved Searches (Future; lower priority)
    - id: STORY-2454
      title: "User can save and retrieve saved searches"
      estimated_points: 3
      dependencies:
        - story_id: "STORY-2451"
          type: "finish_to_start"
      priority: "p3"  # Backlog; implement after core search stable

  capability_mapping:
    capabilities_delivered:
      - capability: "Product Discovery"
        maturity_progression: "2 → 4"  # From manual browsing to AI-assisted search
      - capability: "Search Performance"
        maturity_progression: "1 → 3"  # From no search to optimized search

  metrics:
    total_stories: 4
    total_story_points: 16
    critical_path_length_sprints: 2  # Story 2451 + Story 2452 + Story 2453
    dependency_density: 50%  # 2 out of 4 stories have dependencies
```

---

### Example 3: User Story with Complete Detail

```yaml
UserStory:
  id: "STORY-2451"
  title: "User can search products by keyword"

  # Basic Information
  feature: "FEAT-2084"
  epic: "EPIC-2045"

  # Story Format (required)
  as_a: "casual online shopper"
  i_want: "search products by entering keywords"
  so_that: "I can quickly find what I'm looking for"

  # User Persona
  persona:
    name: "Sarah"
    age: 28
    tech_comfort: "intermediate"
    mobile_usage: "primary"
    search_behavior: "wants results fast; loses patience after 3 seconds"

  # Business Context
  business_value: |
    Search is critical to user experience. Users who can quickly find products
    are 3x more likely to complete purchase. This is foundational for mobile
    commerce platform success.

  estimated_story_points: 5
  estimation_confidence: "high"
  estimation_notes: |
    High confidence: Similar search feature implemented in admin panel.
    Main unknowns around search backend choice resolved in STORY-2426 spike.

  # Dependencies
  dependencies:
    blocking: []  # No stories must complete before this
    enables: ["STORY-2452", "STORY-2453", "STORY-2454"]  # Search is prerequisite
    critical_path: true  # On critical path for MVP delivery

  # Acceptance Criteria (Given-When-Then Format)
  acceptance_criteria:

    criterion_1:
      name: "Search returns matching products"
      scenario_type: "happy_path"
      given:
        - "User is on mobile product search page"
        - "Product database contains 50k products"
        - "Product 'Blue Cotton Shirt' exists with description containing keyword"
      when:
        - "User types 'blue shirt' in search box"
        - "User taps 'Search' button"
      then:
        - "Search completes within 500ms"
        - "Product 'Blue Cotton Shirt' appears in results"
        - "Results show product image, title, price, star rating"
        - "Results paginated (20 products per page)"
      acceptance_test: "Automated; Selenium test; data-driven with 10 test products"

    criterion_2:
      name: "Autocomplete suggestions appear while typing"
      scenario_type: "happy_path"
      given:
        - "User is on search page"
        - "Autocomplete index contains 1000 popular searches"
      when:
        - "User types 'blue' in search box"
        - "System detects user has stopped typing for 200ms"
      then:
        - "Autocomplete dropdown appears within 200ms"
        - "Dropdown shows suggestions: 'blue shirts', 'blue jeans', 'blue shoes'"
        - "Suggestions filtered to text already typed (starts with 'blue')"
        - "User can click suggestion or continue typing"
      acceptance_test: "Automated; tests response time and suggestion accuracy"

    criterion_3:
      name: "Empty results handled gracefully"
      scenario_type: "error_case"
      given:
        - "User is on search page"
      when:
        - "User searches for non-existent product: 'xyzabc'"
      then:
        - "User sees message: 'No products found for xyzabc'"
        - "Message includes suggestion to refine search"
        - "Message shows related categories (e.g., 'Browse similar categories')"
        - "Empty state is friendly (illustration, helpful text)"
      acceptance_test: "Automated; validates message content and UI state"

    criterion_4:
      name: "Search performs well with large dataset"
      scenario_type: "edge_case"
      given:
        - "Product database contains 100k+ products"
      when:
        - "User performs search for popular keyword ('shirt')"
      then:
        - "Search returns results within 500ms"
        - "Results ranked by relevance (exact matches first)"
        - "Results paginated correctly for large result sets"
        - "No timeout errors; graceful degradation if search service slow"
      acceptance_test: "Performance test; LoadRunner script; baseline <500ms"

    criterion_5:
      name: "Search works on slow mobile network"
      scenario_type: "edge_case"
      given:
        - "User on 3G network (simulated: 3Mbps download, 100ms latency)"
      when:
        - "User performs search"
      then:
        - "Search completes within 1000ms (2x baseline for slow network)"
        - "Loading indicator shown for user feedback"
        - "Results cached for offline access (optional)"
      acceptance_test: "Automated; browser dev tools throttle network; test 3G scenario"

  # Definition of Done
  definition_of_done:

    organizational_dod:
      - item: "Code written and peer reviewed"
        status: "required"
        verification: "GitHub PR approved by 2 reviewers"

      - item: "Unit tests written (>70% coverage)"
        status: "required"
        verification: "Jest coverage report shows >70%"

      - item: "Integration tests for API contract"
        status: "required"
        verification: "API contract tests pass; Postman collection"

      - item: "Acceptance criteria tested"
        status: "required"
        verification: "Playwright E2E tests cover all AC; all tests green"

      - item: "Performance acceptable"
        status: "required"
        verification: "Lighthouse score >85; search <500ms response"

      - item: "Security review passed"
        status: "required"
        verification: "SonarQube SAST scan; no critical issues"

      - item: "Documentation updated"
        status: "required"
        verification: "API docs updated in Swagger; README updated"

      - item: "Deployed to staging"
        status: "required"
        verification: "Deploy logs show successful staging deployment"

    team_dod_additions:
      - item: "Mobile tested on actual devices"
        description: "Tested on iPhone 12 (iOS) and Galaxy S21 (Android)"
        status: "required"
        verification: "Test report with screenshots from both devices"

      - item: "Accessibility review"
        description: "Screen reader compatible; ARIA labels correct"
        status: "required"
        verification: "axe DevTools scan; WCAG 2.1 AA compliance"

      - item: "Product Owner acceptance"
        description: "PO verifies feature meets requirements"
        status: "required"
        verification: "PO sign-off in JIRA story"

  # Estimation Details
  estimation:
    story_points: 5
    estimation_breakdown:
      frontend_development: 2
      backend_development: 2
      testing: 0.5
      documentation: 0.5

    planning_poker_session:
      date: "2025-02-10"
      participants: ["dev1", "dev2", "dev3", "qa1", "po"]
      estimates_round_1: [5, 5, 8, 5, "?"]  # PO doesn't estimate
      estimates_round_2: [5, 5, 5, 5]  # After discussion of complexity
      final_estimate: 5
      confidence: "high"
      discussion_notes: |
        Initial 8 estimate noted that search backend selection wasn't finalized.
        Team clarified that spike (STORY-2426) resolved this, so estimate came
        down to 5. Team confident can implement with Elasticsearch integration.

  # Team Assignment
  assigned_to: "dev1@company.com"
  sprint: "Sprint-14"
  sprint_start_date: "2025-02-17"
  sprint_end_date: "2025-03-02"

  # Implementation Tasks
  tasks:
    - id: "TASK-1"
      title: "Create search API endpoint"
      assigned_to: "dev1"
      estimated_hours: 8
      status: "in_progress"

    - id: "TASK-2"
      title: "Implement search UI component (mobile)"
      assigned_to: "dev1"
      estimated_hours: 6
      status: "todo"

    - id: "TASK-3"
      title: "Write unit tests for search service"
      assigned_to: "dev2"
      estimated_hours: 4
      status: "todo"

    - id: "TASK-4"
      title: "Write E2E tests using Playwright"
      assigned_to: "qa1"
      estimated_hours: 5
      status: "todo"

    - id: "TASK-5"
      title: "Performance testing and optimization"
      assigned_to: "dev1"
      estimated_hours: 4
      status: "todo"

  # Risk and Mitigation
  risks:
    - id: "RISK-1"
      description: "Search backend performance unproven with 100k products"
      probability: "medium"
      impact: "high"
      mitigation: "Load test with real data before sprint; compare Elasticsearch vs DB FTS"

    - id: "RISK-2"
      description: "Mobile autocomplete may cause keyboard issues"
      probability: "low"
      impact: "medium"
      mitigation: "Test early with actual mobile devices; prototype if needed"

  # Quality Metrics
  quality_metrics:
    test_coverage_target: 75
    performance_target_ms: 500
    accessibility_wcag_target: "AA"
    mobile_lighthouse_target: 85

  # Splitting Analysis (SPIDR)
  splitting_analysis:
    could_be_split_by:
      - method: "Data (SPIDR-D)"
        idea: "Search 100 products vs 100k products as separate stories"
        recommendation: "No split needed; handle in acceptance criteria as edge case"

      - method: "Interface (SPIDR-I)"
        idea: "Desktop search vs mobile search as separate stories"
        recommendation: "Consider split if mobile UX significantly different"

      - method: "Rules (SPIDR-R)"
        idea: "Keyword search vs relevance ranking as separate stories"
        recommendation: "Keyword search first; add ranking in follow-up story"

    current_recommendation: "Do NOT split; story well-scoped at 5 points"

  # Status and Tracking
  status: "in_progress"
  created_date: "2025-01-15"
  last_updated: "2025-02-18"
  created_by: "product@company.com"

  # Relationships
  related_stories:
    - story_id: "STORY-2426"
      relationship: "depends_on_spike_for_backend_decision"

    - story_id: "STORY-2452"
      relationship: "enables_filtering_feature"

    - story_id: "STORY-2300"
      relationship: "related_web_version; keep in sync"
```

---

## WSJF Prioritization Example

```yaml
Portfolio Backlog - Prioritized by WSJF Score:

Ranking:
  1. Epic-2045: Mobile Commerce Platform
     WSJF: 2.0 (Cost of Delay: 42, Job Size: 21)
     Components: BV=21, TC=13, RR=8, JS=21
     Rationale: Highest value, high criticality, large effort justified by ROI

  2. Feature-1200: Real-time Order Tracking
     WSJF: 1.5 (Cost of Delay: 22, Job Size: 15)
     Components: BV=8, TC=8, RR=6, JS=15
     Rationale: Medium value, time-critical (customer delight), moderate size

  3. Feature-1180: Improved Search Relevance (ML)
     WSJF: 1.3 (Cost of Delay: 13, Job Size: 10)
     Components: BV=8, TC=3, RR=2, JS=10
     Rationale: High value (improves conversion), good ROI, moderate effort

  4. Bug-Fix: Payment Timeout Issue
     WSJF: 1.2 (Cost of Delay: 6, Job Size: 5)
     Components: BV=3, TC=2, RR=1, JS=5
     Rationale: Risk reduction, quick fix, customer satisfaction impact

  5. Feature-1150: Dark Mode
     WSJF: 0.7 (Cost of Delay: 7, Job Size: 10)
     Components: BV=3, TC=2, RR=2, JS=10
     Rationale: Nice-to-have, low business impact, moderate effort; deprioritized

Note: WSJF = (BV + TC + RR) / JS
Lower job size but moderate delay impact = higher priority
Higher job size requires very high business value to justify priority
```

---

## Story Mapping Example

```yaml
StoryMap:
  product_name: "Mobile Commerce Platform"

  # The Backbone: User workflow activities
  backbone:
    - sequence: 1
      activity: "Sign Up / Login"
      user_goal: "Authenticate and access personal account"
      business_value: "Identify users; enable personalization"

    - sequence: 2
      activity: "Browse Products"
      user_goal: "Explore product catalog"
      business_value: "Drive product discovery"

    - sequence: 3
      activity: "Search / Filter"
      user_goal: "Find specific products"
      business_value: "Improve product discovery efficiency"

    - sequence: 4
      activity: "View Product Details"
      user_goal: "Understand product features and reviews"
      business_value: "Reduce purchase uncertainty"

    - sequence: 5
      activity: "Add to Cart / Wishlist"
      user_goal: "Save items for purchase"
      business_value: "Enable impulse control; capture intent"

    - sequence: 6
      activity: "Checkout"
      user_goal: "Purchase items"
      business_value: "Convert intent to revenue"

    - sequence: 7
      activity: "Track Order"
      user_goal: "Monitor delivery status"
      business_value: "Reduce support inquiries; improve confidence"

    - sequence: 8
      activity: "Post-Purchase Engagement"
      user_goal: "Review products; manage returns"
      business_value: "Build loyalty; gather feedback"

  # Ribs: Stories supporting each backbone activity
  # Organized by priority level (1=walking skeleton, 2+=enhancements)

  ribs:
    - activity_id: 1
      activity_name: "Sign Up / Login"
      priority_level: 1  # Critical for walking skeleton
      stories:
        - id: "STORY-100"
          title: "User can sign up with email/password"
          points: 5
          priority: "p1-critical"

        - id: "STORY-101"
          title: "User can log in with email/password"
          points: 3
          priority: "p1-critical"

        - id: "STORY-102"
          title: "User can reset forgotten password"
          points: 3
          priority: "p2-important"

        - id: "STORY-103"
          title: "User can authenticate with social (Google/Apple)"
          points: 5
          priority: "p3-nice-to-have"

    - activity_id: 2
      activity_name: "Browse Products"
      priority_level: 1
      stories:
        - id: "STORY-200"
          title: "User can view product list (home page)"
          points: 5
          priority: "p1-critical"

        - id: "STORY-201"
          title: "User can browse category pages"
          points: 5
          priority: "p1-critical"

        - id: "STORY-202"
          title: "User can see product recommendations"
          points: 8
          priority: "p2-important"

    - activity_id: 3
      activity_name: "Search / Filter"
      priority_level: 1
      stories:
        - id: "STORY-300"
          title: "User can search products by keyword"
          points: 5
          priority: "p1-critical"

        - id: "STORY-301"
          title: "User can filter by category"
          points: 3
          priority: "p1-critical"

        - id: "STORY-302"
          title: "User can filter by price range"
          points: 3
          priority: "p2-important"

    # ... more activities ...

  # Walking Skeleton: Minimum viable product
  walking_skeleton:
    definition: |
      A user can authenticate, browse products, search and filter,
      view details, add to cart, and complete checkout. This represents
      end-to-end functionality allowing first customer purchase.

    included_stories:
      - "STORY-100"  # Sign up
      - "STORY-101"  # Login
      - "STORY-200"  # Browse
      - "STORY-201"  # Categories
      - "STORY-300"  # Search
      - "STORY-301"  # Filter
      - "STORY-400"  # View details
      - "STORY-500"  # Add to cart
      - "STORY-600"  # Checkout

    estimated_duration: "8 sprints"
    business_value: "Enables MVP launch; proves concept"

  # Release Slices: Horizontal cuts for incremental delivery
  release_slices:
    - release_number: 1
      release_name: "MVP"
      target_date: "2026-04-30"
      included_stories:
        # Walking skeleton + essential features
        - "STORY-100"  # Sign up
        - "STORY-101"  # Login
        - "STORY-200"  # Browse
        - "STORY-201"  # Categories
        - "STORY-300"  # Search
        - "STORY-400"  # View details
        - "STORY-500"  # Add to cart
        - "STORY-600"  # Checkout
        - "STORY-601"  # Payment processing
        - "STORY-700"  # Order confirmation email
      estimated_sprints: 8
      business_objectives:
        - "Enable customer purchases on mobile"
        - "Achieve 10% of traffic completing purchases"
        - "Validate mobile commerce viability"

    - release_number: 2
      release_name: "v1.1 - Enhanced Experience"
      target_date: "2026-06-30"
      included_stories:
        - "STORY-102"  # Password reset
        - "STORY-103"  # Social auth
        - "STORY-202"  # Recommendations
        - "STORY-302"  # Price filter
        - "STORY-303"  # Size/color filter
        - "STORY-500"  # Wishlist
        - "STORY-702"  # Order tracking
      estimated_sprints: 4
      business_objectives:
        - "Improve conversion through personalization"
        - "Reduce friction with social auth"
        - "Achieve 25% mobile conversion"

    - release_number: 3
      release_name: "v1.2 - Mobile Optimization"
      target_date: "2026-08-30"
      included_stories:
        # Progressive enhancement; non-critical
        - "STORY-203"  # Infinite scroll
        - "STORY-304"  # Advanced filters (ML-based)"
        - "STORY-500"  # App review/ratings
        - "STORY-703"  # AR try-on"
      estimated_sprints: 3
      business_objectives:
        - "Compete on mobile UX with leaders"
        - "Achieve 30% mobile conversion"

  # Story distribution visualization
  statistics:
    total_stories_identified: 45
    stories_in_walking_skeleton: 9
    stories_for_release_1: 18
    stories_for_release_2: 14
    stories_for_future_releases: 13
    total_estimated_points: 180
    estimated_total_duration_quarters: 3  # Through Release 3
```

---

## Automated Quality Checks (Configuration)

```yaml
# CI/CD Pipeline Definition for DoD Verification

Stages:

  - name: "Code Quality"
    tools:
      - sonarqube
      - eslint
      - prettier
    checks:
      - coverage_minimum: 70
      - no_critical_issues: true
      - no_code_smells: ">10"
    failure_action: "block_merge"

  - name: "Security"
    tools:
      - snyk
      - trivy
      - tfsec
    checks:
      - no_critical_vulnerabilities: true
      - no_secrets_in_code: true
      - sast_passing: true
    failure_action: "block_merge"

  - name: "Testing"
    tools:
      - jest
      - playwright
      - postman
    checks:
      - unit_tests_passing: true
      - integration_tests_passing: true
      - e2e_tests_passing: true
      - coverage_minimum: 70
    failure_action: "block_merge"

  - name: "Performance"
    tools:
      - lighthouse
      - new_relic
      - loadtest
    checks:
      - response_time: "<500ms"
      - lighthouse_score: ">85"
      - no_performance_regression: true
    failure_action: "block_merge"

  - name: "Staging Deployment"
    tools:
      - docker
      - kubernetes
      - gitlab_ci
    checks:
      - deploy_successful: true
      - smoke_tests_passing: true
      - feature_accessible: true
    failure_action: "block_merge"

# INVEST Compliance Checking

invest_validation:
  independent:
    check: "Count dependencies; alert if >2"
    scoring_rule: "5=no deps, 4=1-2 deps, 3=3-4 deps, 2=5+ deps, 1=blocks many"

  negotiable:
    check: "Verify story is not locked specification"
    scoring_rule: "5=flexible, 3=some constraints, 1=locked"

  valuable:
    check: "Ensure user value statement exists"
    scoring_rule: "5=clear value, 3=indirect value, 1=no value"

  estimable:
    check: "Verify team estimated and confidence high"
    scoring_rule: "5=high conf, 3=medium conf, 1=cannot estimate"

  small:
    check: "Alert if >8 points"
    scoring_rule: "5=≤5 pts, 4=5-8 pts, 2=>8 pts, 1=>13 pts"

  testable:
    check: "Verify AC are measurable; alert if vague"
    scoring_rule: "5=all clear, 3=some vague, 1=not testable"

# Dashboard Configuration

dashboard_metrics:
  team_invest_compliance:
    definition: "Average INVEST score of backlog items"
    target: "≥24/30"
    trend_display: true

  dod_compliance_rate:
    definition: "% of stories meeting full DoD"
    target: "≥95%"
    trend_display: true

  stories_at_risk:
    definition: "Stories failing INVEST or with unresolved dependencies"
    display: "List and fix recommendations"

  velocity_trend:
    definition: "3-sprint rolling average"
    display: "Chart with confidence band"

  dependency_complexity:
    definition: "Critical path length and complexity"
    display: "Network diagram; alert if >4 deep"
```

---

## Summary: Implementation Sequence

**Week 1-2: Foundation**
- [ ] Create epic hypothesis statement template
- [ ] Create lean business case template
- [ ] Set up INVEST compliance checker in JIRA

**Week 3-4: Story Specification**
- [ ] Implement Given-When-Then acceptance criteria format
- [ ] Create Definition of Done checklist
- [ ] Train team on INVEST scoring

**Week 5-6: Estimation**
- [ ] Establish Fibonacci story point scale
- [ ] Run first Planning Poker session
- [ ] Start velocity tracking

**Week 7-8: Advanced Patterns**
- [ ] Implement SPIDR splitting guidance
- [ ] Create story dependency tracking
- [ ] Add cost of delay calculations

**Week 9-10: Automation**
- [ ] Implement CI/CD DoD verification
- [ ] Create automated INVEST compliance dashboard
- [ ] Build dependency visualization

---

## Key Metrics to Track

1. **Quality Metrics**
   - INVEST compliance score (target: ≥24/30)
   - DoD compliance rate (target: ≥95%)
   - Test coverage (target: ≥70%)
   - Code review cycle time

2. **Delivery Metrics**
   - Velocity stability (target: ±20% variance)
   - Sprint commitment vs. delivery (target: ≥95%)
   - Sprint goals completion (target: ≥90%)

3. **Business Value Metrics**
   - WSJF score vs. actual value delivered
   - Feature adoption rate
   - Customer satisfaction (NPS) by feature
   - Revenue contribution by feature

4. **Risk Metrics**
   - Critical path length
   - Dependency density
   - Stories returned from done (target: <5%)
   - Unplanned work percentage (target: <15%)

---

**Implementation Guide Version**: 1.0
**Last Updated**: January 29, 2026
