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Coding — Functional Specification

Version: v1.2.3 | Status: Active | Last Updated: March 2026

Purpose

The coding module provides a unified interface for code execution, sandboxing, review, monitoring, static analysis, pattern matching, and debugging. It consolidates secure code execution and automated code analysis capabilities into a cohesive structure.

This module is critical for the "Verification" phase of the AI coding loop, allowing the system to test its own code without risking the host environment, and for continuous quality assessment through automated code review.

Design Principles

Modularity

  • Submodule Separation: Clear separation between execution, sandboxing, review, and monitoring concerns
  • Isolation Providers: Abstract the sandbox mechanism (Docker, gVisor, or simple venv for trusted mode)
  • Execution Interface: Standard API (execute_code()) regardless of backend
  • Analyzer Interface: Unified API for static_analysis and pattern_matching backends
  • Review Interface: Integrated review API that leverages various analysis sub-modules

Internal Coherence

  • Result Standardization: All executions return a standard ExecutionResult (stdout, stderr, exit_code, duration, status)
  • Review Standardization: All reviews return standardized AnalysisResult objects
  • Consistent Error Handling: Unified error types and handling across submodules

Parsimony

  • Dependencies: Should rely on containerization module for heavy lifting if using Docker
  • Shared Infrastructure: Common logging and monitoring infrastructure

Functionality

  • Timeouts: Derived from configuration with sensible defaults
  • Resource Limits: Prevent fork bombs or memory exhaustion
  • Quality Gates: Configurable thresholds for code quality enforcement
  • Comprehensive Analysis: Multiple analysis types (quality, security, performance, etc.)

Architecture

graph TD
    Request[Code Request] --> Guard[Security Guard]
    Guard --> Execution[Execution Submodule]
    Execution --> Sandbox[Sandbox Submodule]
    Sandbox -->|Docker| Container[Docker Container]
    Sandbox -->|Local| Venv[Restricted Venv]
    Container --> Monitor[Monitoring Submodule]
    Venv --> Monitor
    Monitor --> Result[Execution Result]
    
    Result -->|Failure| Debugger[Debugging Submodule]
    Debugger --> ErrorAnalyzer[Error Analyzer]
    ErrorAnalyzer --> PatchGen[Patch Generator]
    PatchGen --> Verifier[Fix Verifier]
    Verifier -->|Success| Result

    Code --> Static[Static Analysis Submodule]
    Code --> Patterns[Pattern Matching Submodule]
    Static --> Review[Review Submodule]
    Patterns --> Review
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Functional Requirements

Execution & Sandboxing

  1. Run Multiple Languages: Execute code in Python, JavaScript, Java, C/C++, Go, Rust, Bash
  2. File Access: Mount specific directories as read-only or read-write
  3. Network Control: Block or allow network access (default block)
  4. Resource Limits: Enforce CPU, memory, and time constraints
  5. Session Management: Support persistent execution environments

Static Analysis (Consolidated)

  1. Linting: Automated code style and error checking
  2. Complexity Analysis: Measuring cyclomatic complexity and maintainability
  3. Security Scanning: Searching for common security patterns and vulnerabilities
  4. Metrics: Logical lines of code, comment density, etc.

Pattern Matching (Consolidated)

  1. Structural Search: Find code patterns based on AST nodes
  2. Clones Detection: Identify duplicated or near-duplicate code blocks
  3. Refactoring detection: Identify common refactoring patterns across versions
  4. AST-based transformations: Perform safe code modifications based on patterns

Monitoring

  1. Resource Tracking: Monitor CPU, memory, execution time
  2. Execution Monitoring: Track execution status and completion
  3. Metrics Collection: Aggregate metrics for analysis

Autonomous Debugging

  1. Error Analysis: Parse execution outputs to identify error types and locations
  2. Patch Generation: Generate potential fixes for identified errors
  3. Fix Verification: Verify patches in a sandboxed environment
  4. Closed Loop: Orchestrate the cycle of execution -> failure -> diagnosis -> patch -> verify

Quality Standards

  • Security: "Secure by Design". Default to least privilege
  • Cleanup: Ephemeral containers/envs must be destroyed after use
  • Actionable Feedback: Review feedback must identify location (line number) and suggestion

Interface Contracts

Public API

Execution:

  • execute_code(language: str, code: str, stdin: Optional[str] = None, timeout: Optional[int] = None, session_id: Optional[str] = None) -> dict[str, Any]

Review:

  • analyze_file(file_path: str, analysis_types: list[str] = None) -> list[AnalysisResult]
  • analyze_project(project_root: str, target_paths: list[str] = None, analysis_types: list[str] = None) -> AnalysisSummary
  • check_quality_gates(project_root: str, thresholds: dict[str, int] = None) -> QualityGateResult
  • generate_report(project_root: str, output_path: str, format: str = "html") -> bool

Monitoring:

  • ResourceMonitor - Track resource usage
  • ExecutionMonitor - Monitor execution status
  • MetricsCollector - Collect and aggregate metrics

Static Analysis:

  • StaticAnalyzer - Perform advanced linting and complexity checks
  • SecurityScanner - Scan for vulnerabilities
  • MetricCollector - Gather code statistics

Pattern Matching:

  • PatternMatcher - Search for structural code patterns
  • CloneDetector - Find duplicate code
  • ASTTransformer - Pattern-based refactorings

Debugging:

  • Debugger - Main orchestration for the debug loop
  • ErrorAnalyzer - Parse and diagnose errors
  • PatchGenerator - Generate code patches
  • FixVerifier - Verify patches in sandbox

Dependencies

  • Modules: containerization, logging_monitoring
  • System: Docker (optional but recommended for sandboxing)
  • Tools: pyscn (for advanced code analysis)

Implementation Guidelines

Usage Patterns

  • Always check Docker availability before executing code
  • Use resource limits for all executions
  • Validate inputs before processing
  • Always clean up temporary files and containers
  • Use monitoring to track execution metrics

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