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c2cb41b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | from pydantic import BaseModel, Field
from typing import List, Optional, Dict
from datetime import datetime, date
class DeliveryHealthMetrics(BaseModel):
"""Metrics for delivery health analysis"""
sprint_id: Optional[str] = None
sprint_name: Optional[str] = None
period_start: date
period_end: date
# Velocity Metrics
planned_story_points: float = 0
completed_story_points: float = 0
velocity: float = 0
velocity_trend: float = 0 # percentage change from previous period
# Completion Metrics
total_issues: int = 0
completed_issues: int = 0
completion_rate: float = 0
# Time Metrics
avg_cycle_time_hours: float = 0
avg_lead_time_hours: float = 0
# Quality Indicators
blocked_issues_count: int = 0
overdue_issues_count: int = 0
reopened_issues_count: int = 0
# Risk Indicators
at_risk_issues: int = 0
health_score: float = 0 # 0-100
class ProductivityMetrics(BaseModel):
"""Workforce productivity metrics"""
team_member_id: str
team_member_name: str
period_start: date
period_end: date
# Activity Metrics
issues_completed: int = 0
story_points_completed: float = 0
code_commits: int = 0 # from GitHub
pull_requests: int = 0 # from GitHub
# Time Metrics
total_hours_logged: float = 0
avg_hours_per_day: float = 0
# Efficiency Metrics
avg_issue_completion_time_hours: float = 0
productivity_score: float = 0 # 0-100
# Workload Metrics
current_assigned_issues: int = 0
current_story_points: float = 0
utilization_rate: float = 0 # percentage
class CostEfficiencyMetrics(BaseModel):
"""Cost and efficiency analysis"""
period_start: date
period_end: date
project_key: Optional[str] = None
# Resource Metrics
total_team_members: int = 0
total_hours_logged: float = 0
estimated_cost: float = 0 # based on hours
# Output Metrics
features_delivered: int = 0
story_points_delivered: float = 0
# Efficiency Ratios
cost_per_feature: float = 0
cost_per_story_point: float = 0
hours_per_story_point: float = 0
# Waste Indicators
blocked_time_hours: float = 0
rework_hours: float = 0
waste_percentage: float = 0
class TeamCapacityMetrics(BaseModel):
"""Team capacity and utilization"""
team_id: Optional[str] = None
team_name: str
period_start: date
period_end: date
# Capacity Metrics
total_capacity_hours: float = 0
allocated_hours: float = 0
available_hours: float = 0
utilization_rate: float = 0
# Workload Distribution
team_members: List[Dict] = Field(default_factory=list)
overloaded_members: int = 0
underutilized_members: int = 0
# Sprint Metrics
current_sprint_load: float = 0
forecasted_capacity: float = 0
class RiskAlert(BaseModel):
"""Risk and alert model"""
alert_id: str
alert_type: str # delivery_delay, cost_overrun, resource_shortage, quality_issue
severity: str # critical, high, medium, low
title: str
description: str
affected_entity: str # sprint, project, team_member
entity_id: str
detected_at: datetime
suggested_action: Optional[str] = None
metrics: Dict = Field(default_factory=dict)
class InsightRecommendation(BaseModel):
"""AI-generated insights and recommendations"""
insight_id: str
category: str # delivery, productivity, cost, resource
title: str
description: str
confidence_score: float = 0 # 0-1
impact_level: str # high, medium, low
recommendations: List[str] = Field(default_factory=list)
supporting_data: Dict = Field(default_factory=dict)
generated_at: datetime
class KanbanFlowMetrics(BaseModel):
"""Kanban flow efficiency metrics"""
board_id: int
board_name: str
period_start: date
period_end: date
# Flow Metrics
throughput: int = 0 # Issues completed in period
avg_cycle_time_days: float = 0
avg_lead_time_days: float = 0
flow_efficiency: float = 0 # 0-100
# WIP Metrics
current_wip: int = 0
avg_wip: float = 0
wip_violations: int = 0 # Number of times WIP limits were exceeded
# Column Metrics
bottleneck_column: Optional[str] = None
bottleneck_score: float = 0
# Predictability
throughput_variance: float = 0
cycle_time_variance: float = 0
# Health Score
flow_health_score: float = 0 # 0-100
class KanbanColumnAnalysis(BaseModel):
"""Analysis for a specific Kanban column"""
column_name: str
statuses: List[str]
# Current State
current_issue_count: int = 0
wip_limit_min: Optional[int] = None
wip_limit_max: Optional[int] = None
is_over_wip_limit: bool = False
# Flow Metrics
avg_time_in_column_days: float = 0
throughput: int = 0 # Issues exited this column
# Efficiency
utilization_rate: float = 0 # current / max limit
is_bottleneck: bool = False
bottleneck_score: float = 0 # Higher = more of a bottleneck
class KanbanCumulativeFlow(BaseModel):
"""Cumulative flow diagram data"""
board_id: int
period_start: date
period_end: date
# Daily snapshots
data_points: List[Dict] = Field(default_factory=list)
# Each data point: {date, column_name, issue_count}
class WIPLimitRecommendation(BaseModel):
"""WIP limit optimization recommendations"""
column_name: str
current_limit: Optional[int]
recommended_min: int
recommended_max: int
reasoning: str
confidence_score: float = 0 # 0-1
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