🎯 DETAILED BUG DESCRIPTION

Bug Type: Data Logic & Display Inconsistency
Component: Vacant Shifts Detection Algorithm
Impact: Critical - Users cannot see all available shifts

📍 Navigation Path

Bug Location:

Path: Home → My Schedules → Custom Schedule → Main Schedule Page

• This is where the "Vacant Shifts" section is displayed

• This is where the bug occurs (missing shifts)

Data Source for Validation:

Path: [Archived Schedule Page Location]

• This contains the built schedule data

• This is the source of truth for what shifts are actually filled

• Vacant shifts should be calculated based on gaps in this built schedule

User Type: Employee

🐛 ROOT CAUSE ANALYSIS

Current Problem:

The "Vacant Shifts" list on the Main Schedule Page is not displaying all unfilled shift slots from the built schedule. The system is missing shifts that should be identified as "holes" or gaps in the schedule.

Expected Behavior:

The vacant shifts algorithm should:

1. Analyze the built/archived schedule data

2. Identify ALL unfilled time slots across all days and shift types

3. Display every gap as an available vacant shift on the main schedule page

Specific Example Scenario:

Sunday Schedule Status (from built/archived schedule):✅ Morning Shift: FILLED (assigned to employee)❌ Afternoon Shift: VACANT (should appear in vacant shifts list)✅ Night Shift: FILLED (assigned to employee)📋 Expected Result: Sunday Afternoon should appear in "Vacant Shifts" on Main Schedule Page📋 Current Bug: Sunday Afternoon is missing from vacant shifts list

🔍 TECHNICAL REQUIREMENTS

Algorithm Logic Fix:

1. Schedule Analysis:

   • Parse the built/archived schedule data

   • Identify all possible shift slots (Day × Shift Type combinations)

   • Cross-reference with actual assignments

2. Gap Detection:

   • For each day of the week (Sunday through Saturday)

   • For each shift type (Morning, Afternoon, Night)

   • If no employee is assigned → Mark as VACANT

3. Display Logic:

   • Show ALL unassigned shift combinations on Main Schedule Page

   • Format: "[Day] [Shift Type]" (e.g., "Sunday Afternoon")

   • Sort chronologically for better UX

Data Sources:

• Primary: Built/Archived Schedule data

• Reference: Complete shift matrix (Days × Shift Types)

• Output: Comprehensive vacant shifts list on Main Schedule Page

🧪 TESTING SCENARIOS

Test Case 1: Partial Day Coverage

Given: Monday has Morning + Night shifts filled (in built schedule)When: User views vacant shifts on Main Schedule PageThen: Monday Afternoon should appear in vacant shifts list

Test Case 2: Complete Day Gap

Given: Tuesday has no shifts assigned (in built schedule)When: User views vacant shifts on Main Schedule PageThen: Tuesday Morning, Afternoon, AND Night should all appear

Test Case 3: Mixed Week Scenario

Given: Week with various filled/unfilled combinations (in built schedule)When: Algorithm processes the scheduleThen: Only actual gaps should appear in Main Schedule Page vacant shiftsAnd: No false positives/negatives

Test Case 4: Fully Scheduled Week

Given: All shifts for all days are filled (in built schedule)When: User views vacant shifts on Main Schedule PageThen: List should be empty or show "No vacant shifts"

Test Case 5: Edge Cases

Given: Various edge cases (holidays, weekends, partial weeks)When: Algorithm processes the scheduleThen: All valid gaps should still be detected correctly

🎯 SUCCESS CRITERIA

Functional Requirements:

• ✅ 100% Gap Detection: Every unfilled shift slot appears in Main Schedule Page vacant shifts

• ✅ Zero False Positives: No filled shifts appear as vacant

• ✅ Data Source Accuracy: Uses built/archived schedule data exclusively

• ✅ Complete Coverage: All days and shift types included in analysis

• ✅ Real-time Updates: Vacant shifts update when schedule changes

Technical Validation:

• ✅ Algorithm Testing: Unit tests for gap detection logic

• ✅ Data Integrity: Correct built schedule data parsing

• ✅ UI Consistency: Vacant shifts display matches actual availability from built schedule

• ✅ Performance: Efficient processing of schedule data

📊 BUSINESS IMPACT

Current Issue Impact:

• User Confusion: Employees cannot see all available shifts

• Scheduling Gaps: Unfilled shifts remain hidden and unassigned

• Process Inefficiency: Manual verification required to find true vacancies

• Missed Opportunities: Available shifts go unfilled due to poor visibility

Post-Fix Benefits:

• Complete Visibility: All vacant shifts properly identified

• Improved Scheduling: Better shift coverage and assignment

• User Confidence: Reliable and accurate vacancy information

• Operational Efficiency: Streamlined shift management

🔧 IMPLEMENTATION GUIDANCE

Key Areas to Investigate:

1. Schedule Data Processing:

   • Review how built/archived schedule data is parsed for Main Schedule Page

   • Verify shift matrix definition (days × shift types)

   • Check for filtering logic that might exclude valid gaps

2. Vacant Shift Logic:

   • Examine the gap detection algorithm used for Main Schedule Page

   • Look for incomplete shift type coverage

   • Test boundary conditions (weekends, holidays)

   • Ensure algorithm uses built schedule data exclusively

3. Data Flow Validation:

   • Trace from built/archived schedule → gap detection → Main Schedule Page display

   • Verify no data is lost in processing pipeline

   • Ensure real-time updates when schedule changes

Priority Focus:

• High: Fix core gap detection algorithm for Main Schedule Page

• Medium: Improve UI display of vacant shifts

• Low: Performance optimization of processing

📱 PLATFORMS TO TEST

• Mobile: iOS and Android FlutterFlow apps

• Web: Browser-based interface

• Verification: Compare Main Schedule Page vacant shifts with built/archived schedule data

📋 DEFINITION OF DONE

• ✅ All unfilled shifts from built/archived schedule appear in Main Schedule Page vacant shifts list

• ✅ No filled shifts incorrectly show as vacant

• ✅ Algorithm handles all day/shift type combinations

• ✅ Main Schedule Page displays vacant shifts clearly and completely

• ✅ Testing confirms accuracy across multiple schedule scenarios

• ✅ Performance remains acceptable with larger datasets

• ✅ Real-time updates work when schedule data changes

Estimated Time: 3-5 hours (depending on algorithm complexity)