Python API to Unified Coverage Interoperability Standard (UCIS) Data Model

• Python API for manipulating UCIS coverage databases
• CLI Tools for database conversion, merging, and reporting
• MCP Server for AI agent integration (see MCP_SERVER.md)
• AgentSkills Support for enhanced AI agent understanding (see https://agentskills.io)
• Support for XML, YAML, and UCIS binary formats
• Multiple export formats: LCOV, Cobertura, JaCoCo, Clover

# Standard installation
pip install pyucis

# With MCP server support
pip install pyucis[dev]

# Convert coverage database
pyucis convert --input-format xml --output-format yaml input.xml -o output.yaml

# Merge multiple databases
pyucis merge db1.xml db2.xml db3.xml -o merged.xml

# Generate reports
pyucis report coverage.xml -o report.txt

# Interactive Terminal UI (NEW!)
pyucis view coverage.xml

# Query coverage information
pyucis show summary coverage.xml
pyucis show gaps coverage.xml --threshold 80
pyucis show covergroups coverage.xml

PyUCIS now includes an interactive Terminal User Interface (TUI) for exploring coverage databases:

# Launch the TUI
pyucis view coverage.xml

Features:

• Dashboard View - High-level coverage overview with statistics
• Hierarchy View - Navigate design structure with interactive tree
• Gaps View - Identify uncovered items for test planning
• Hotspots View - Priority-based improvement targets with P0/P1/P2 classification
• Metrics View - Statistical analysis, distributions, and quality indicators
• Help System - Comprehensive keyboard shortcuts (press ?)
• Color-coded coverage indicators (red <50%, yellow 50-80%, green 80%+)
• Keyboard-driven navigation optimized for terminal workflows
• Fast, responsive interface using Rich library

Keyboard Shortcuts:

• 1 - Dashboard view
• 2 - Hierarchy view
• 3 - Gaps view
• 4 - Hotspots view
• 5 - Metrics view
• ↑/↓ - Navigate items
• ←/→ - Collapse/expand tree nodes
• ? - Help overlay
• q - Quit

Hotspots Analysis: The Hotspots view intelligently identifies high-priority coverage targets:

• P0/P1 (Critical/High): Low coverage modules (<50%) requiring immediate attention
• P1/P2 (High/Medium): Near-complete items (90%+) - low hanging fruit
• P1 (High): Completely untested coverpoints

Metrics & Statistics: The Metrics view provides in-depth analysis:

• Coverage distribution by hit count (0, 1-10, 11-100, 100+)
• Statistical measures (mean, median, min, max)
• Quality indicators (complete, high, medium, low tiers)
• Bin utilization and zero-hit ratios

The TUI provides an efficient alternative to HTML reports for real-time coverage analysis, especially useful for:

• Quick coverage assessment during verification
• Identifying coverage gaps without generating static reports
• Priority-driven test planning with hotspot analysis
• Remote terminal sessions over SSH
• CI/CD pipeline integration

PyUCIS now includes a Model Context Protocol (MCP) server that enables AI agents to interact with coverage databases:

# Start the MCP server
pyucis-mcp-server

See MCP_SERVER.md for detailed documentation on:

• Available MCP tools (17+ coverage analysis tools)
• Integration with Claude Desktop and other AI platforms
• API usage examples

PyUCIS includes an AgentSkills skill definition that provides LLM agents with comprehensive information about PyUCIS capabilities. When you run any PyUCIS command, the absolute path to the skill file is displayed, allowing agents to reference it for better understanding of UCIS coverage data manipulation.

# Running any pyucis command displays the skill file location
pyucis --help
# Output includes: Skill Definition: /path/to/ucis/share/SKILL.md

from ucis import UCIS

# Open a database
db = UCIS("coverage.xml")

# Access coverage data
for scope in db.scopes():
    print(f"Scope: {scope.name}")
    for coveritem in scope.coveritems():
        print(f"  {coveritem.name}: {coveritem.count} hits")

• MCP Server Documentation
• UCIS Standard

Apache 2.0

• PyPI
• GitHub
• Issue Tracker