1. MLIR Debugger: Symbolic Execution for Compilers
1.1 Core Technology
The MLIR Debugger represents a significant advancement in compiler debugging technology.
Unlike traditional debuggers that operate on concrete values, this system uses symbolic
execution to explore multiple execution paths simultaneously.
1.2 Key Features
- Multi-Level Debugging: Works across all MLIR abstraction levels, from high-level operations to low-level machine code
- Symbolic Execution: Uses symbolic values to represent many possible concrete values, enabling path exploration
- Automated Test Generation: Automatically generates test cases covering edge cases and failure modes
- Constraint Solving: Integrates with Z3 theorem prover for advanced constraint solving
- DAP Protocol Support: Industry-standard Debug Adapter Protocol for IDE integration
- AI Agent Integration: Structured debugging API designed for AI coding assistants
1.3 Architecture
The debugger follows a modular microservices architecture:
- Debugger Core: Symbolic execution engine and constraint solver
- DAP Server: Debug Adapter Protocol implementation for IDE integration
- MLIR Parser: MLIR code analysis and transformation tracking
- Test Generator: Automated test case generation based on symbolic execution
- Reporting Engine: Comprehensive debugging reports and analytics
2. MLIR MCP Server: Model Context Protocol Integration
2.1 MCP Protocol Support
The MLIR MCP Server implements the Model Context Protocol (MCP) to provide structured
access to MLIR debugging capabilities for AI agents and development tools.
2.2 Protocol Versions
- MCP 2024: Initial implementation with SSE transport
- MCP 2025-11-25: Latest protocol version with enhanced capabilities
- Backward Compatibility: Support for multiple protocol versions simultaneously
2.3 Key Endpoints
- /sse: Server-Sent Events endpoint for MCP 2024 clients
- /messages: HTTP messaging endpoint for MCP 2024
- /mcp: SSE endpoint for MCP 2025-11-25 clients
- /mcp-messages: HTTP messaging endpoint for MCP 2025-11-25
2.4 Transport Layer
The server supports multiple transport mechanisms:
- SSE (Server-Sent Events): Efficient streaming for real-time updates
- HTTP Messaging: Traditional request-response pattern
- Streamable HTTP: Advanced streaming capabilities (MCP 2025-11-25)
3. Integration with Dark Factories
3.1 Automated Debugging Workflow
In dark factory environments, the MLIR Debugger enables fully automated debugging workflows:
- Automatic Issue Detection: Continuous monitoring of MLIR transformations for issues
- Intelligent Test Generation: Automated creation of test cases for detected issues
- Symbolic Analysis: Deep analysis of compiler transformations using symbolic execution
- Automated Fix Generation: Suggested fixes based on symbolic execution results
3.2 AI Agent Integration
AI coding agents use the debugger through structured APIs:
- Structured Debugging API: Clean, well-documented API for agent interaction
- Context-Aware Debugging: Debugging sessions maintain context across agent interactions
- Automated Reporting: Comprehensive debug reports in agent-readable format
- Integration with Agent Workflows: Seamless integration into agent development cycles
3.3 Continuous Testing Integration
The debugger integrates with CI/CD pipelines for continuous testing:
- Automated Regression Detection: Identifies regressions in compiler transformations
- Coverage Analysis: Measures test coverage for MLIR code
- Performance Benchmarking: Tracks performance changes across compiler versions
- Security Scanning: Identifies potential security issues in compiler code
4. Deployment Architecture
4.1 Kubernetes Deployment
The system is designed for production deployment on Kubernetes:
- Containerized Components: Each component runs in isolated Docker containers
- Horizontal Scaling: Components scale independently based on load
- High Availability: Multiple replicas with automatic failover
- Resource Management: Efficient resource allocation and utilization
4.2 Security Architecture
- TLS Encryption: All communications encrypted with TLS 1.3
- RBAC Authorization: Role-based access control for all components
- Secret Management: Secure credential management via Kubernetes secrets
- Audit Logging: Comprehensive audit trails for all operations
- Vulnerability Scanning: Regular security scanning of container images
4.3 Monitoring & Observability
- Health Monitoring: Continuous health checks for all components
- Performance Metrics: Real-time performance monitoring
- Log Aggregation: Centralized logging for troubleshooting
- Alerting System: Automated alerts for issues and anomalies
5. Technical Implementation Details
5.1 Symbolic Execution Engine
The core symbolic execution engine implements:
- Path Exploration: Systematic exploration of execution paths
- Constraint Collection: Collection of path constraints during execution
- Constraint Solving: Solving constraints using Z3 theorem prover
- Test Case Generation: Generating concrete test cases from symbolic execution
5.2 MLIR Analysis
- IR Parsing: Parsing and analysis of MLIR code
- Transformation Tracking: Tracking compiler transformations across levels
- Dependency Analysis: Analysis of data and control dependencies
- Pattern Matching: Matching and analysis of MLIR patterns
5.3 Protocol Implementation
- MCP Protocol Handler: Implementation of MCP protocol specifications
- Transport Layer: Multiple transport mechanisms (SSE, HTTP, Streamable HTTP)
- Protocol Negotiation: Automatic protocol version negotiation
- Backward Compatibility: Support for older protocol versions