Create 10 GitHub issues for Drools risk rules threshold rebalancing analysis

[Copilot]

Description

Created comprehensive issue suite analyzing Drools risk rule thresholds against actual data distributions. Current risk scoring shows severe imbalance: 82.5% of politicians cluster in MEDIUM risk (30-49 points), only 6% reach HIGH risk (50+), indicating thresholds don't align with real behavioral patterns.

Analysis scope: 50 risk rules across 4 entity types (politician/party/committee/ministry), 84 database views, 150+ sample data distribution files.

Type of Change

Primary Changes

• 🔄 Enhancement

Political Analysis

• 📊 Political Data Analysis
  • Party Analysis
  • Committee Processing
  • Government Data
  • Riksdagen Integration
• 📈 Analytics & Metrics
  • Risk Assessment
  • Decision Analysis

Technical Changes

• 📝 Documentation
  • Technical Documentation

Impact Analysis

Political Analysis Impact

• Data quality: Identified 8 rules (committee/ministry) defined but never executed due to incomplete RulesEngineImpl
• Analysis accuracy: Current thresholds arbitrary - absence rates cluster 14-16% but threshold set at 20%, committee productivity < 20 docs/year vs actual 102-412
• Transparency features: Enables statistically-justified risk differentiation using P25/P50/P75 percentiles

Technical Impact

• Performance impact: None - documentation/analysis only
• Security implications: None
• Dependency changes: None

Testing

• Political data validation completed

Documentation

• Package/module documentation updated
• Political analysis documentation updated

Related Issues

EPIC: #8189 - Comprehensive Drools Risk Rules Balance

Analysis Issues Created:

• ⚖️ Balance Politician Risk Score Distribution - High Risk Overclustering at 6% #8179 - Politician risk distribution (82.5% MEDIUM overclustering)
• 🏛️ Validate Committee Productivity Thresholds Against Activity Distributions #8180 - Committee productivity thresholds (< 20 vs 102-412 actual)
• 📊 Calibrate Party Effectiveness Thresholds Using Performance Distribution Data #8181 - Party effectiveness calibration (win rates, docs/member)
• 🏢 Optimize Ministry Risk Thresholds by Portfolio Type and Legislative Cycle #8182 - Ministry thresholds by portfolio type
• 🗳️ Refine Voting Participation Thresholds Based on 14-16% Absence Clustering #8183 - Voting participation (14-16% absence clustering)
• 📄 Adjust Document Productivity Thresholds from Annual Trend Analysis (4,200 docs/year baseline) #8184 - Document productivity (4,200 docs/year baseline)
• 🤝 Validate Collaboration Thresholds with Coalition Alignment Data #8185 - Collaboration thresholds (coalition context)
• 🔴 Calibrate Rebel Rate Thresholds with Party Discipline Context (5%/10%/20% validation) #8186 - Rebel rate calibration (5%/10%/20% validation)
• ⚖️ Optimize Combined Risk Score Weighting - Salience-Based Aggregation Analysis #8187 - Combined risk score weighting (salience aggregation)
• 🔧 Complete RulesEngineImpl - Add Committee and Ministry Rule Evaluation #8188 - RulesEngineImpl completion (missing committee/ministry insertion)

Checklist

• Code follows project coding standards
• Comments are clear and helpful
• Documentation is updated
• Changes are backward compatible

Additional Notes

Key Findings:

• Risk Score Gap: 6% HIGH / 82.5% MEDIUM / 11.5% LOW (target: 20/60/20)
• Threshold Misalignment: Voting absence 14-16% actual vs 20% threshold, committee < 20 docs/year vs 102-412 actual
• Missing Execution: 8 rules defined but RulesEngineImpl.java only inserts politicians/parties, not committees/ministries
• Arbitrary Values: Most thresholds lack statistical validation against sample data distributions

Sample Data Analysis:

distribution_risk_score_buckets.csv:
  50-69 (High):   24 politicians (6.00%)   avg=52.00
  30-49 (Medium): 330 politicians (82.50%)  avg=39.97
  10-29 (Low):    46 politicians (11.50%)   avg=10.30

distribution_behavioral_patterns_by_party.csv:
  MODERATE_RISK:     avg_absence_rate = 15.98%
  STANDARD_BEHAVIOR: avg_absence_rate = 14.07%
  (Current threshold: 20% - misses 14-16% clustering)

Implementation Roadmap:

1. Calculate P25/P50/P75/P90 percentiles for all metrics
2. Segment thresholds by context (government/opposition, party size, portfolio type)
3. Update .drl files with data-driven thresholds
4. Implement committee/ministry insertion in RulesEngineImpl.java
5. Validate improved distribution and measure Gini coefficient

Security Considerations

• No sensitive data exposed
• Security best practices followed
• Compliance requirements met

Release Notes

Created 10 GitHub issues analyzing Drools risk rule threshold imbalances. Identified 82.5% MEDIUM risk overclustering, missing committee/ministry rule execution, and arbitrary thresholds lacking statistical validation. Issues provide data-driven analysis with percentile-based recommendations for rebalancing all 50 risk rules across politician, party, committee, and ministry entities.

Original prompt

Create 10 github issues without assigning them to copilot. Link with pr with good descriptions and icons.

Want to improve src/main/resources/com/hack23/cia/service/impl/rules to be more balanced by analysing distributions and sample data in service.data.impl/sample-data/distinct_values and service.data.impl/sample-data

Analyse completion of service.impl/src/main/java/com/hack23/cia/service/impl/rules/RulesEngineImpl.java and jpa model objects in service.data.impl/src/main/resources/META-INF/persistence.xml and full-schema.sql to understand current schema.

Context DATABASE_VIEW_INTELLIGENCE_CATALOG.md , DROOLS_RISK_RULES.md , CHANGELOG_INTELLIGENCE.md

All to support business BUSINESS_PRODUCT_DOCUMENT.md

Custom agent used: hack23-cia-taskagent
CIA product specialist creating GitHub issues for political intelligence platform, optimizing UI/UX, code quality, and ensuring ISMS compliance through comprehensive analysis

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[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud