coverity-metrics 1.0.0

Comprehensive metrics and dashboard generator for Coverity static analysis

Coverity Metrics

A Python-based project to generate comprehensive metrics from Coverity's PostgreSQL database.

Overview

This tool analyzes Coverity static analysis data stored in PostgreSQL and generates various metrics to help you understand code quality, defect trends, and development team activity.

Features

The tool provides the following metric categories:

1. Defect Metrics

• Total Defects by Project: Count of defects grouped by project with active/fixed breakdown
• Defects by Severity: Distribution across High/Medium/Low impact levels
• Defects by Category: Top defect categories (e.g., Security, Null pointer, Resource leak)
• Defects by Checker: Specific checkers finding the most defects
• Defect Density: Defects per 1000 lines of code (KLOC) by project/stream
• File Hotspots: Files with the highest concentration of defects

2. Triage Metrics

• Defects by Triage Status: Distribution by action (Fix Required, Ignore, etc.)
• Defects by Classification: Bug, False Positive, Intentional, etc.
• Defects by Owner: Defect ownership and assignment statistics

3. Code Quality Metrics

• Code Metrics by Stream: Lines of code, comment ratios, file counts
• Function Complexity: Distribution of cyclomatic complexity
• Most Complex Functions: Identify high-complexity functions needing refactoring
• Comment Ratio: Code documentation percentage

4. Trend Metrics

• Weekly Defect Trend: Defect count trends over time
• Weekly File Count Trend: Codebase growth tracking
• Snapshot History: Analysis run history with defect changes

5. User Activity Metrics

• Login Statistics: User engagement with the system
• Active Triagers: Most active users in defect triage
• Session Analytics: Average session duration per user

6. Performance Metrics (NEW!)

• Database Statistics: Database size and growth tracking
• Commit Performance: Analysis duration (min/max/average times)
• Snapshot Performance: Recent commit performance with queue times
• Defect Discovery Rate: Daily/weekly defect discovery trends
• System Analytics: Largest tables, resource utilization

7. Summary Metrics

• Overall counts: projects, streams, defects, files, functions, LOC
• High severity defect counts
• Active user counts

Installation

From Source (Recommended)

# Clone or download this repository
git clone https://github.com/yourusername/coverity-metrics.git
cd coverity-metrics

# Install the package with all dependencies
pip install -e .

This installs the package in editable mode, making the CLI commands (coverity-dashboard, coverity-metrics, coverity-export) available system-wide.

From PyPI (Future)

# When published to PyPI
pip install coverity-metrics

Requirements

The package includes these dependencies (automatically installed):

• psycopg2-binary - PostgreSQL database adapter
• pandas - Data analysis and manipulation
• matplotlib - Plotting library
• seaborn - Statistical data visualization
• python-dateutil - Date/time utilities
• openpyxl - Excel file support for CSV exports
• jinja2 - HTML template engine for dashboard generation
• plotly - Interactive charts and visualizations
• tqdm - Progress bars

Configuration

The tool requires configuration through config.json. Create this file with your Coverity instance(s) connection details:

cp config.json.example config.json
# Edit config.json with your database credentials

Configuration File Format

{
  "instances": [
    {
      "name": "Production",
      "description": "Production Coverity Instance",
      "enabled": true,
      "database": {
        "host": "coverity-server.company.com",
        "port": 5432,
        "database": "cim",
        "user": "coverity_ro",
        "password": "your_password_here"
      },
      "color": "#2c3e50"
    }
  ]
}

Important:

• Add at least one instance with "enabled": true
• For single-instance mode: Configure one instance
• For multi-instance mode: Configure 2+ instances (auto-detected)
• Add config.json to .gitignore to protect credentials

Database Schema

The tool works with the following key Coverity database tables:

• defect, stream_defect, defect_instance - Defect information
• checker, checker_properties - Checker and severity data
• triage_state, defect_triage - Triage information
• stream, stream_file, stream_function - Code structure
• snapshot - Analysis snapshots
• project, project_stream - Project organization
• users, user_login - User activity
• weekly_issue_count, weekly_file_count - Trend data

Usage

After installation, you can use the package in two ways: Command-Line Interface (CLI) or Python Library.

Command-Line Interface (CLI)

The package provides three CLI commands for different use cases:

Command	Purpose	Output	Best For
coverity-dashboard	Visual HTML dashboard	Interactive HTML files with charts	Presentations, visual analysis, sharing
coverity-metrics	Console text report	Terminal output (stdout)	Quick checks, CI/CD, piping
coverity-export	Data export	CSV files	Excel analysis, archiving, integrations

Key Differences:

• coverity-dashboard: Creates beautiful interactive HTML dashboards with Plotly charts, saved to output/ directory. Auto-opens in browser for easy viewing. Supports multi-instance aggregation.

• coverity-metrics: Prints all metrics as formatted text tables directly to your terminal. No files created. Great for quick command-line checks or redirecting to log files (coverity-metrics > report.txt).

• coverity-export: Exports raw metric data to timestamped CSV files in exports/ directory. Perfect for importing into Excel, Power BI, or custom analysis tools.

Note: All three tools require direct PostgreSQL database access. CSV exports cannot be used as input to generate dashboards—they're export-only for external analysis.

---

1. Generate Dashboard (Main Tool)

# Basic usage - auto-detects instance type from config.json
coverity-dashboard

# Filter by specific project across all instances
coverity-dashboard --project "MyProject"

# Generate for specific instance only
coverity-dashboard --instance Production

# Change trend analysis period (default: 365 days)
coverity-dashboard --days 180

# Custom output folder
coverity-dashboard --output reports/2026

# Enable caching for better performance
coverity-dashboard --cache --cache-ttl 86400

# Generate without opening browser
coverity-dashboard --no-browser  

# Use different configuration file
coverity-dashboard --config my-config.json

Auto-Detection Behavior:

• config.json is required with at least one enabled instance configured
• If config.json has 2+ enabled instances: Multi-instance mode (generates aggregated + per-instance + per-project dashboards)
• If config.json has 1 enabled instance: Single-instance mode (generates dashboard for that instance)
• Use --project to filter by specific project only
• Use --instance to generate for specific instance only (multi-instance mode)
• Use --single-instance-mode to force single-instance behavior even with multiple instances

For all options: coverity-dashboard --help

2. Console Metrics Report

Outputs: Text tables printed to terminal (no files created)

# Generate console metrics report
coverity-metrics

# With options
coverity-metrics --project MyProject --no-cache

# Redirect to file
coverity-metrics > daily-report.txt

Use Cases:

• Quick command-line checks
• Automated CI/CD pipelines
• SSH sessions without GUI
• Piping to log files or other tools

3. CSV Export

Outputs: Timestamped CSV files in exports/ directory

# Export metrics to CSV
coverity-export

# Custom output directory
coverity-export --output exports/

Files Created:

• defects_by_project_YYYYMMDD_HHMMSS.csv
• defects_by_severity_YYYYMMDD_HHMMSS.csv
• defect_density_YYYYMMDD_HHMMSS.csv
• file_hotspots_YYYYMMDD_HHMMSS.csv
• code_metrics_YYYYMMDD_HHMMSS.csv
• ...and more

Use Cases:

• Excel pivot tables and analysis
• Power BI / Tableau dashboards
• Custom Python/R data analysis
• Archiving historical metrics
• Third-party tool integrations

---

Typical Workflow

Daily Quick Check:

# Fast terminal check
coverity-metrics

Weekly Team Review:

# Generate visual dashboard for presentation
coverity-dashboard --cache
# Opens interactive HTML in browser

Monthly Executive Report:

# Visual dashboard
coverity-dashboard --days 90 --cache

# Export data for custom Excel charts
coverity-export

Complete Analysis Workflow:

# 1. Quick overview in terminal
coverity-metrics

# 2. Generate interactive dashboard
coverity-dashboard --cache --no-browser

# 3. Export raw data for deep analysis
coverity-export

# Now you have:
# - Console output for quick reference
# - HTML dashboard (output/dashboard.html) for presentations
# - CSV files (exports/*.csv) for custom Excel analysis

Python Library Usage

You can also use the package programmatically in your Python code:

from coverity_metrics import CoverityMetrics, MultiInstanceMetrics, InstanceConfig

# Single instance usage
metrics = CoverityMetrics(
    connection_params={
        'host': 'localhost',
        'port': 5432,
        'database': 'coverity',
        'user': 'postgres',
        'password': 'your_password'
    },
    project_filter='MyProject'  # Optional
)

# Get metrics
defect_metrics = metrics.get_defect_metrics()
print(defect_metrics)

# Multi-instance usage
instances = [
    InstanceConfig("Production", {...connection_params...}),
    InstanceConfig("Development", {...connection_params...})
]

multi = MultiInstanceMetrics(instances)
aggregated = multi.get_aggregated_metrics()

See INSTALL.md for detailed API examples.

Dashboard Features

• Project Filtering: View metrics for all projects or filter by specific project
• Project Navigation: Easy navigation between project-specific dashboards
• Tabbed Interface: Organized into Overview, Code Quality, and Performance tabs
• Summary cards with key metrics
• Interactive charts for severity distribution, project comparison
• File hotspots with detailed tables
• Code quality metrics visualization
• Function complexity distribution
• Top defect checkers and categories
• Performance metrics (NEW!):
  • Database size and statistics
  • Commit/analysis performance (min/max/average times)
  • Recent snapshot performance with queue times
  • Defect discovery rate trends
  • Largest database tables
• Responsive design for mobile/tablet viewing
• Print-friendly layout

Dashboard Files Generated:

• output/dashboard.html - Global view of all projects
• output/dashboard_{ProjectName}.html - Project-specific dashboards

Multi-Instance Support (NEW - Automatic!)

For environments with multiple Coverity instances, the tool now auto-detects your configuration:

Configure multiple Coverity instances in config.json:

{
  "instances": [
    {
      "name": "Production",
      "description": "Production Coverity Instance",
      "enabled": true,
      "database": {
        "host": "coverity-prod.company.com",
        "port": 5432,
        "database": "cim",
        "user": "coverity_ro",
        "password": "your_password"
      },
      "color": "#2c3e50"
    },
    {
      "name": "Development",
      "description": "Development Coverity Instance",
      "enabled": true,
      "database": {
        "host": "coverity-dev.company.com",
        "port": 5432,
        "database": "cim",
        "user": "coverity_ro",
        "password": "your_password"
      },
      "color": "#3498db"
    }
  ],
  "aggregated_view": {
    "enabled": true,
    "name": "All Instances"
  }
}

Simplified Multi-Instance Commands:

# Generate everything - automatically creates:
#   - Aggregated dashboard across all instances
#   - Individual dashboard for each instance
#   - Project dashboards for all projects in each instance
coverity-dashboard

# Filter by specific project across all instances
coverity-dashboard --project MyApp

# Generate for specific instance only (with all its projects)
coverity-dashboard --instance Production

# Generate specific project on specific instance only
coverity-dashboard --instance Production --project MyApp

# Use custom configuration file
coverity-dashboard --config my-config.json

What Gets Generated Automatically:

When you run coverity-dashboard with a multi-instance config.json:

1. Aggregated Dashboard (output/dashboard_aggregated.html) - Combined view of all instances
2. Instance Dashboards (output/{InstanceName}/dashboard.html) - One per instance
3. Project Dashboards (output/{InstanceName}/dashboard_{ProjectName}.html) - All projects for each instance

Multi-Instance Dashboard Features:

• Aggregated View: Combined metrics from all Coverity instances
• Instance Comparison Charts: Side-by-side defect count comparison
• Color-Coded Instances: Visual differentiation of instances
• Cross-Instance Project List: All projects with instance attribution
• Per-Instance Dashboards: Individual dashboards for each instance
• Instance Filtering: Navigate between instances easily

For detailed multi-instance setup and usage, see MULTI_INSTANCE_GUIDE.md

Performance & Caching

For large deployments with many instances/projects, enable caching to dramatically improve performance:

# Enable caching (24-hour TTL by default)
coverity-dashboard --cache

# Custom cache TTL (48 hours)
coverity-dashboard --cache --cache-ttl 48

# View cache statistics
coverity-dashboard --cache-stats

# Clear expired cache entries
coverity-dashboard --clear-cache

# Force refresh (bypass cache)
coverity-dashboard --no-cache

Performance Benefits:

• First run: Same time as without caching (cache is built)
• Subsequent runs: 90-95% faster (uses cached data)
• Example: 30 minutes → 2 minutes for 10 instances × 100 projects

Progress Tracking for Large Operations:

# Enable progress tracking (for resumable operations)
coverity-dashboard --cache --track-progress

# Resume interrupted session
coverity-dashboard --cache --resume SESSION_ID

For detailed caching configuration, performance tuning, and troubleshooting, see CACHING_GUIDE.md

Export to CSV

Export all metrics to CSV files:

coverity-export

This creates timestamped CSV files in the exports/ directory for Excel analysis.

Use Individual Metrics

You can also use the metrics module programmatically:

from metrics import CoverityMetrics

# Initialize
metrics = CoverityMetrics()

# Get specific metrics
defects_by_severity = metrics.get_defects_by_severity()
print(defects_by_severity)

# Get defect density
density = metrics.get_defect_density_by_project()
print(density)

# Get file hotspots
hotspots = metrics.get_file_hotspots(limit=10)
print(hotspots)

# Get overall summary
summary = metrics.get_overall_summary()
for key, value in summary.items():
    print(f"{key}: {value}")

Available Metric Methods

All methods return pandas DataFrames for easy manipulation:

Defect Metrics:

• get_total_defects_by_project()
• get_defects_by_severity()
• get_defects_by_checker_category(limit=20)
• get_defects_by_checker_name(limit=20)
• get_defect_density_by_project()
• get_file_hotspots(limit=20)

Triage Metrics:

• get_defects_by_triage_status()
• get_defects_by_classification()
• get_defects_by_owner(limit=20)

Code Quality Metrics:

• get_code_metrics_by_stream()
• get_function_complexity_distribution()
• get_most_complex_functions(limit=20)

Trend Metrics:

• get_defect_trend_weekly(weeks=12)
• get_file_count_trend_weekly(weeks=12)
• get_snapshot_history(stream_name=None, limit=20)

User Activity:

• get_user_login_statistics(days=30)
• get_most_active_triagers(days=30, limit=10)

Performance Metrics:

• get_database_statistics() - Database size and statistics
• get_largest_tables(limit=10) - Largest database tables by size
• get_snapshot_performance(limit=20) - Recent commit/analysis performance
• get_commit_time_statistics() - Commit time averages and statistics
• get_defect_discovery_rate(days=30) - Defect discovery trends over time

Summary:

• get_overall_summary()
• get_available_projects() - List all available projects

Recommended Metrics for Different Use Cases

For Management/Executive Reports:

1. Overall Summary - High-level statistics
2. Defects by Severity - Risk assessment
3. Defect Density by Project - Quality comparison across projects
4. Weekly Defect Trend - Progress over time
5. Defects by Triage Status - Workload and backlog

For Development Teams:

1. File Hotspots - Identify problematic files
2. Most Complex Functions - Refactoring candidates
3. Defects by Category - Common error patterns
4. Defects by Owner - Individual workload
5. Snapshot History - Analysis run results

For Quality Assurance:

1. Defects by Checker - Tool effectiveness
2. Defects by Classification - False positive rate
3. Code Metrics by Stream - Code coverage
4. Function Complexity - Code maintainability
5. Defect Density - Quality benchmarks

For Team Leads:

1. Active Triagers - Team engagement
2. Defects by Owner - Work distribution
3. User Login Statistics - Tool adoption
4. Weekly Trends - Team velocity

Project Structure

coverity_metrics/
├── config.json            # Database configuration (create from config.json.example)
├── config.json.example    # Configuration template
├── db_connection.py       # Database connection handling
├── metrics.py             # Metrics calculation logic
├── multi_instance_metrics.py  # Multi-instance support
├── generate_dashboard.py  # Dashboard generator (main entry point)
├── main.py                # CLI metrics report
├── export_metrics.py      # CSV export utility
├── requirements.txt       # Python dependencies
├── templates/             # HTML dashboard templates
├── static/                # CSS/JS assets
└── README.md             # This file

Extending the Tool

You can easily add new metrics by extending the CoverityMetrics class:

class CoverityMetrics:
    # ... existing methods ...
    
    def get_custom_metric(self):
        """Your custom metric description"""
        query = """
            SELECT ...
            FROM ...
        """
        results = self.db.execute_query_dict(query)
        return pd.DataFrame(results)

Troubleshooting

Database Connection Issues

• Verify PostgreSQL is running: Check Coverity services
• Check credentials in config.json
• Ensure PostgreSQL port (default 5432) is accessible
• Verify at least one instance is enabled in config.json

Missing Data

• Some metrics may return empty if:
  • No snapshots have been committed
  • Streams haven't been analyzed
  • Defects haven't been triaged

Performance

• For large databases, some queries may take time
• Consider adding database indexes on frequently queried columns
• Use the limit parameter to restrict result sizes

Security Notes

• Database passwords are stored in config.json
• Always add config.json to .gitignore before committing
• Use read-only database credentials when possible
• Set appropriate file system permissions on config.json
• Never commit database credentials to version control

# Recommended file permissions (Linux/Mac)
chmod 600 config.json

# Add to .gitignore
echo "config.json" >> .gitignore

• Use environment variables or secure vaults in production

License

This tool is provided as-is for use with Coverity installations.

Support

For issues or questions:

1. Check the Coverity documentation for database schema details
2. Review the SQL queries in metrics.py to understand data sources
3. Use schema_explorer.py to investigate your specific database structure