Analytics & Reporting

Comprehensive guide to using ConductorQA's analytics features, creating reports, and gaining insights from your testing data.

Overview

ConductorQA provides powerful analytics capabilities to help you understand testing patterns, identify improvement opportunities, and demonstrate the value of your QA efforts. This guide covers all available analytics features and how to use them effectively.

Analytics Dashboard Overview

Home Dashboard

Organization-Wide Metrics

Dashboard Overview:
├── Test Execution Summary: Total runs, pass rates, trends
├── Project Health: Status across all projects
├── Team Performance: Individual and collective metrics
├── Quality Trends: Historical quality indicators
└── Resource Utilization: Capacity and workload analysis

Key Performance Indicators (KPIs)

  • Total Test Suites: Across all projects in organization
  • Total Test Cases: Comprehensive test coverage count
  • Total Test Runs: Historical execution volume
  • Average Success Rate: Organization-wide quality metric
  • Average Execution Time: Performance efficiency indicator

Project-Specific Analytics

Project Dashboard Features

Project Analytics:
├── Test Run History: Chronological execution records
├── Pass/Fail Trends: Quality progression over time
├── Test Suite Performance: Individual suite effectiveness
├── Team Activity: Member contribution tracking
└── Environment Analysis: Testing across different environments

Interactive Visualizations

  • Daily Test Runs Chart: Pass/fail breakdown with trend analysis
  • Success Rate Trend: Quality trajectory over time
  • Execution Time Analysis: Performance optimization insights
  • Test Distribution: Workload and coverage visualization

Time-Based Analytics

Flexible Time Periods

Standard Time Ranges

  • 7 Days: Short-term trend analysis and daily monitoring
  • 30 Days: Monthly performance review and planning
  • 90 Days: Quarterly assessment and strategic planning
  • 1 Year: Annual review and long-term trend analysis

Custom Date Ranges

// Example: Custom analytics query
const analyticsData = {
  "period": "custom",
  "start_date": "2025-06-01",
  "end_date": "2025-08-28",
  "metrics": ["pass_rate", "execution_time", "test_volume"],
  "breakdown": "daily" // daily, weekly, monthly
};

Trend Analysis

Quality Trend Indicators

  • Improving: Consistent upward trend in pass rates
  • Stable: Consistent performance within acceptable range
  • Declining: Downward trend requiring attention
  • Volatile: High variability indicating instability

Performance Metrics

Performance Analysis:
├── Execution Velocity: Tests completed per time period
├── Cycle Time: Time from test creation to completion
├── Defect Detection Rate: Issues found per test run
├── Resolution Time: Average time to fix failing tests
└── Regression Rate: Percentage of tests that regress

Advanced Analytics Features

Flaky Test Detection

Identification Criteria

Flaky Test Detection Algorithm:
{
  "criteria": {
    "inconsistent_results": "Pass/fail variation >20% over 10 runs",
    "environment_sensitivity": "Different results across environments",
    "timing_dependencies": "Failures correlate with execution time",
    "external_dependencies": "Failures during service outages"
  },
  "severity_levels": {
    "high": "Fails >50% with no code changes",
    "medium": "Fails 20-50% with no code changes", 
    "low": "Occasional failures <20%"
  }
}

Flaky Test Management

  • Automatic Detection: AI-powered identification of unreliable tests
  • Root Cause Analysis: Common patterns and failure reasons
  • Stabilization Recommendations: Suggested fixes and improvements
  • Tracking and Monitoring: Progress on flaky test remediation

Test Coverage Analysis

Coverage Metrics

Coverage Dimensions:
├── Requirement Coverage: Tests mapped to requirements
├── Feature Coverage: Functionality validation completeness
├── Code Coverage: Integration with development metrics
├── Risk Coverage: High-risk area validation
└── User Journey Coverage: End-to-end scenario validation

Coverage Visualization

  • Heat Maps: Visual representation of coverage density
  • Gap Analysis: Identification of untested areas
  • Overlap Detection: Redundant test identification
  • Priority Mapping: Coverage aligned with business priorities

Team Performance Analytics

Individual Metrics

Tester Performance Dashboard:
├── Test Execution Rate: Tests completed per day/week
├── Quality Score: Accuracy of test execution and results
├── Review Participation: Peer review contributions
├── Knowledge Sharing: Documentation and mentoring activities
└── Skill Development: Learning progress and certifications

Team Collaboration Metrics

  • Communication Frequency: Comments, reviews, discussions
  • Knowledge Transfer: Documentation quality and sharing
  • Cross-Training: Skill overlap and backup coverage
  • Conflict Resolution: Issue escalation and resolution rates

Custom Reports and Dashboards

Report Templates

Executive Summary Report

# Test Management Executive Summary

## Period: [Date Range]

### Key Metrics
- **Test Execution**: 1,247 tests executed (+15% vs previous period)
- **Quality Rate**: 94.2% pass rate (+2.1% improvement)
- **Coverage**: 87% requirement coverage (+5% increase)
- **Team Productivity**: 45 tests/person/week (+8% improvement)

### Quality Highlights
- Zero critical defects escaped to production
- 12% reduction in flaky tests through stabilization efforts
- 25% improvement in test execution speed

### Risk Areas
- Payment module showing 15% failure rate (investigation ongoing)
- Mobile testing coverage at 65% (target: 80%)
- 3 team members require additional training on new features

### Recommendations
1. Increase focus on payment module testing
2. Expand mobile testing team capacity
3. Implement advanced automation for repetitive scenarios

Technical Report Template

# Technical Testing Report

## Test Automation Metrics
- **Automation Coverage**: 67% (target: 75%)
- **Automated Test Success Rate**: 96.8%
- **Manual Test Success Rate**: 91.2%
- **Average Execution Time**: 
  - Automated: 2.3 minutes/test
  - Manual: 8.7 minutes/test

## Environment Analysis
- **Production-like Environment**: 98% uptime
- **Staging Environment**: 95% uptime  
- **Development Environment**: 87% uptime

## Defect Analysis
- **Total Defects Found**: 89
- **Critical**: 2 (both resolved)
- **High**: 15 (12 resolved, 3 in progress)
- **Medium**: 34 (28 resolved, 6 in progress) 
- **Low**: 38 (30 resolved, 8 in backlog)

Custom Dashboard Creation

Dashboard Components

Custom Dashboard Configuration:
{
  "widgets": [
    {
      "type": "metric_card",
      "title": "Success Rate",
      "metric": "pass_rate",
      "period": "30_days",
      "target": 95
    },
    {
      "type": "trend_chart", 
      "title": "Daily Test Execution",
      "metrics": ["total_tests", "passed_tests", "failed_tests"],
      "period": "90_days"
    },
    {
      "type": "pie_chart",
      "title": "Test Distribution",
      "breakdown": "priority",
      "period": "current_sprint"
    },
    {
      "type": "table",
      "title": "Top Failing Tests",
      "columns": ["test_name", "failure_rate", "last_failure"],
      "limit": 10
    }
  ]
}

Widget Types Available

  • Metric Cards: Single KPI displays with trend indicators
  • Line Charts: Time-series data visualization
  • Bar Charts: Comparative analysis across categories
  • Pie Charts: Distribution and proportion analysis
  • Heat Maps: Density and intensity visualization
  • Tables: Detailed data listings with sorting and filtering

Integration Analytics

CI/CD Pipeline Metrics

Build Quality Correlation

Pipeline Integration Metrics:
├── Build Success Rate: Tests passing vs build failures
├── Deployment Frequency: Release cadence and quality
├── Lead Time: Feature development to production
├── Recovery Time: Time to fix failed deployments
└── Change Failure Rate: Percentage of deployments causing issues

Test Automation ROI

  • Time Savings: Manual vs automated execution comparison
  • Cost Analysis: Resource allocation and efficiency gains
  • Quality Improvement: Defect detection rate comparison
  • Scalability Benefits: Capacity increases without proportional resource growth

External Tool Integration

Supported Integrations

Analytics Data Sources:
├── Version Control: Git commit and merge data
├── Issue Tracking: Jira/GitHub Issues defect correlation
├── Performance Monitoring: Application performance correlation
├── User Analytics: Customer impact and satisfaction data
└── Business Intelligence: Revenue and business metric correlation

Data Export and Sharing

Export Formats

Supported Export Types

  • PDF Reports: Professional formatted reports for stakeholders
  • Excel/CSV: Raw data for custom analysis and manipulation
  • JSON/API: Programmatic access for external systems
  • Image Exports: Charts and visualizations for presentations

Automated Report Distribution

Report Automation Configuration:
{
  "schedule": "weekly", // daily, weekly, monthly
  "recipients": [
    "qa-team@company.com",
    "engineering-leads@company.com", 
    "product-owners@company.com"
  ],
  "format": "pdf",
  "template": "executive_summary",
  "filters": {
    "projects": ["web-platform", "mobile-app"],
    "priority": ["critical", "high"]
  }
}

Sharing and Collaboration

Dashboard Sharing Options

  • Public Links: Shareable URLs for stakeholder access
  • Embedded Widgets: Integration into external dashboards
  • Email Summaries: Regular metric updates via email
  • Slack Integration: Real-time notifications and summaries

Performance Optimization

Analytics Performance

Query Optimization

  • Data Indexing: Optimized database queries for fast results
  • Caching Strategy: Frequently accessed data cached for speed
  • Incremental Loading: Progressive data loading for large datasets
  • Background Processing: Heavy analytics computed asynchronously

Best Practices for Large Datasets

Large Dataset Management:
├── Time Range Limits: Use appropriate date ranges for analysis
├── Filter Usage: Apply filters to reduce data volume
├── Aggregation Levels: Use summary data for overview analysis
├── Progressive Loading: Load detailed data on demand
└── Cache Utilization: Leverage cached results when possible

Advanced Analytics Techniques

Predictive Analytics

Quality Prediction Models

Predictive Quality Indicators:
{
  "quality_risk_factors": [
    "recent_code_changes", 
    "developer_experience_level",
    "feature_complexity_score",
    "historical_defect_density",
    "testing_time_available"
  ],
  "prediction_models": {
    "defect_probability": "85% accuracy",
    "execution_time": "92% accuracy", 
    "resource_requirements": "78% accuracy"
  }
}

Trend Forecasting

  • Pass Rate Projections: Predict future quality trends
  • Resource Planning: Forecast testing capacity needs
  • Risk Assessment: Identify potential quality issues early
  • Release Readiness: Predict deployment success probability

Statistical Analysis

Statistical Methods Used

  • Correlation Analysis: Relationship between variables
  • Regression Analysis: Trend prediction and modeling
  • Standard Deviation: Variability and consistency measurement
  • Confidence Intervals: Reliability of predictions and estimates

Troubleshooting Analytics Issues

Common Analytics Problems

Data Accuracy Issues

Problem: Metrics don’t match expected values Solutions:

  • Verify data filters and time ranges
  • Check for incomplete test runs or data
  • Validate calculation methods and formulas
  • Review data synchronization status

Performance Issues

Problem: Slow dashboard loading or timeouts Solutions:

  • Reduce date range for analysis
  • Apply more specific filters
  • Use aggregated data instead of detailed records
  • Clear browser cache and refresh

Missing Data

Problem: Expected data not appearing in analytics Solutions:

  • Check project and test suite permissions
  • Verify data collection is properly configured
  • Confirm test runs have completed successfully
  • Review data retention policies

Data Quality Assurance

Data Validation Process

Data Quality Checks:
├── Completeness: All expected data points present
├── Accuracy: Values match source systems
├── Consistency: Data relationships are logical
├── Timeliness: Data is current and up-to-date
└── Integrity: No corruption or missing references

Best Practices for Analytics

Effective Dashboard Design

Dashboard Design Principles

  • Purpose-Driven: Each dashboard serves specific stakeholder needs
  • Visual Hierarchy: Most important metrics prominently displayed
  • Actionable Insights: Data presented enables decision-making
  • Context Awareness: Historical context and benchmarks provided
  • Mobile Friendly: Accessible across devices and screen sizes

Stakeholder-Specific Views

Dashboard Audiences:
├── QA Team: Detailed execution metrics and test health
├── Development Team: Defect trends and integration metrics
├── Product Owners: Feature quality and release readiness  
├── Management: Strategic metrics and ROI analysis
└── Executives: High-level KPIs and business impact

Metrics Strategy

Leading vs Lagging Indicators

Metric Categories:
├── Leading Indicators: Predict future outcomes
│   ├── Test case review velocity
│   ├── Automation coverage growth
│   ├── Team skill development progress
│   └── Process adoption rates
└── Lagging Indicators: Measure past outcomes
    ├── Defect escape rates
    ├── Customer satisfaction scores  
    ├── Release cycle times
    └── Business impact metrics

Next Steps

To maximize your analytics capabilities:

  1. Set Up Custom Dashboards - Create stakeholder-specific views
  2. Configure Automated Reports - Set up regular distribution
  3. Implement Best Practices - Follow proven analytics strategies
  4. Integrate External Tools - Connect with your existing toolchain

Ready to unlock insights from your testing data? Start by exploring the pre-built dashboards, then gradually customize them to match your specific reporting needs and stakeholder requirements.

Last updated: August 28, 2025

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