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81e3673 | 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 | #!/usr/bin/env python3
"""
Phase 171 Coverage Gap Analysis Script
Analyzes Phase 171 baseline coverage data to create a realistic roadmap
for achieving 80% backend coverage based on historical performance.
Author: Phase 171 Plan 04A
Date: 2026-03-12
"""
import json
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Any
def load_coverage_data() -> Dict[str, Any]:
"""Load Phase 171 overall coverage data."""
coverage_path = Path("tests/coverage_reports/backend_phase_171_overall.json")
with open(coverage_path) as f:
return json.load(f)
def load_zero_coverage_analysis() -> Dict[str, Any]:
"""Load zero coverage analysis data."""
zero_cov_path = Path("tests/coverage_reports/metrics/zero_coverage_analysis.json")
with open(zero_cov_path) as f:
return json.load(f)
def categorize_files_by_tier(coverage_data: Dict[str, Any]) -> Dict[str, List[Dict]]:
"""
Categorize files by coverage tier.
Tier 1: Zero coverage (highest priority)
Tier 2: < 20% coverage
Tier 3: 20-50% coverage
Tier 4: > 50% coverage (lowest priority)
"""
tier1_critical = [] # Zero coverage
tier2_high = [] # < 20%
tier3_medium = [] # 20-50%
tier4_low = [] # > 50%
for file_info in coverage_data['files']:
cov = file_info['line_coverage']
if cov == 0.0:
tier1_critical.append(file_info)
elif cov < 20.0:
tier2_high.append(file_info)
elif cov < 50.0:
tier3_medium.append(file_info)
else:
tier4_low.append(file_info)
return {
'tier1_critical': tier1_critical,
'tier2_high': tier2_high,
'tier3_medium': tier3_medium,
'tier4_low': tier4_low
}
def calculate_historical_performance() -> Dict[str, Any]:
"""
Calculate historical performance from Phases 165-170.
Returns:
Dict with avg_gain_per_phase, avg_duration_min, avg_lines_per_phase
"""
historical_phases = {
'Phase 165': {'gain': 4.0, 'duration_min': 5, 'notes': 'Governance & LLM (isolated)'},
'Phase 166': {'gain': 0.0, 'duration_min': 5, 'notes': 'Episodic Memory (blocked)'},
'Phase 167': {'gain': 3.5, 'duration_min': 7, 'notes': 'API Routes'},
'Phase 168': {'gain': 5.0, 'duration_min': 5, 'notes': 'Database Layer'},
'Phase 169': {'gain': 4.5, 'duration_min': 25, 'notes': 'Tools & Integrations'},
'Phase 170': {'gain': 3.0, 'duration_min': 8, 'notes': 'LanceDB, WebSocket, HTTP'}
}
total_gain = sum(p['gain'] for p in historical_phases.values())
total_duration = sum(p['duration_min'] for p in historical_phases.values())
num_phases = len(historical_phases)
return {
'phases_analyzed': num_phases,
'avg_gain_per_phase': total_gain / num_phases,
'avg_duration_min': total_duration / num_phases,
'historical_data': historical_phases
}
def calculate_roadmap_metrics(
coverage_data: Dict[str, Any],
historical_perf: Dict[str, Any]
) -> Dict[str, Any]:
"""Calculate roadmap metrics for reaching 80% coverage."""
current_coverage = coverage_data['line_coverage']
lines_covered = coverage_data['lines_covered']
lines_total = coverage_data['lines_total']
target_coverage = 80.0
gap_percent = target_coverage - current_coverage
lines_needed = int((target_coverage / 100) * lines_total) - lines_covered
avg_gain_per_phase = historical_perf['avg_gain_per_phase']
avg_duration_min = historical_perf['avg_duration_min']
phases_needed = int(gap_percent / avg_gain_per_phase) + 1
estimated_weeks = phases_needed / 5 # Assuming 5 phases per week
estimated_hours = (phases_needed * avg_duration_min) / 60
return {
'gap_percent': gap_percent,
'lines_needed': lines_needed,
'phases_needed': phases_needed,
'estimated_weeks': estimated_weeks,
'estimated_hours': estimated_hours
}
def print_analysis_summary(
coverage_data: Dict[str, Any],
zero_cov_data: Dict[str, Any],
tiers: Dict[str, List[Dict]],
historical_perf: Dict[str, Any],
roadmap_metrics: Dict[str, Any]
) -> None:
"""Print comprehensive analysis summary."""
current_coverage = coverage_data['line_coverage']
lines_covered = coverage_data['lines_covered']
lines_total = coverage_data['lines_total']
print("=" * 80)
print("PHASE 171 COVERAGE GAP ANALYSIS")
print("=" * 80)
print()
print("CURRENT COVERAGE (Phase 171 Baseline):")
print(f" Line Coverage: {current_coverage:.2f}%")
print(f" Lines Covered: {lines_covered:,}")
print(f" Total Lines: {lines_total:,}")
print()
print("GAP TO 80% TARGET:")
print(f" Gap: {roadmap_metrics['gap_percent']:.2f} percentage points")
print(f" Lines Needed: {roadmap_metrics['lines_needed']:,}")
print()
print("FILE INVENTORY:")
print(f" Total Files: {len(coverage_data['files']):,}")
print(f" Zero Coverage Files: {zero_cov_data['total_zero_coverage_files']:,}")
print(f" Zero Coverage Lines: {zero_cov_data['total_lines_uncovered']:,}")
below_80 = len([f for f in coverage_data['files'] if f['line_coverage'] < 80])
print(f" Below 80%: {below_80:,} files")
print()
print("HISTORICAL PERFORMANCE (Phases 165-170):")
for phase, data in historical_perf['historical_data'].items():
print(f" {phase}: +{data['gain']:.1f}% (~{data['duration_min']} min) - {data['notes']}")
print(f" AVERAGE: +{historical_perf['avg_gain_per_phase']:.2f}% per phase "
f"(~{historical_perf['avg_duration_min']:.1f} min)")
print()
print("ROADMAP CALCULATION:")
print(f" Recommended phases to reach 80%: {roadmap_metrics['phases_needed']} phases")
print(f" Estimated duration: {roadmap_metrics['estimated_weeks']:.1f} weeks")
print(f" Estimated effort: {roadmap_metrics['estimated_hours']:.1f} hours")
print()
print("FILE TIER BREAKDOWN:")
print(f" Tier 1 (Critical - Zero Coverage): {len(tiers['tier1_critical']):,} files")
print(f" Tier 2 (High - < 20% Coverage): {len(tiers['tier2_high']):,} files")
print(f" Tier 3 (Medium - 20-50% Coverage): {len(tiers['tier3_medium']):,} files")
print(f" Tier 4 (Low - > 50% Coverage): {len(tiers['tier4_low']):,} files")
print()
def main():
"""Main execution function."""
print("Loading coverage data...")
coverage_data = load_coverage_data()
zero_cov_data = load_zero_coverage_analysis()
print("Categorizing files by tier...")
tiers = categorize_files_by_tier(coverage_data)
print("Calculating historical performance...")
historical_perf = calculate_historical_performance()
print("Calculating roadmap metrics...")
roadmap_metrics = calculate_roadmap_metrics(coverage_data, historical_perf)
print()
print_analysis_summary(
coverage_data,
zero_cov_data,
tiers,
historical_perf,
roadmap_metrics
)
print("Analysis complete.")
print(f"Generated at: {datetime.utcnow().isoformat()}Z")
if __name__ == "__main__":
main()
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