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Technical Debt Assessment

Overview

This document outlines our approach to measuring, assessing, and visualizing technical debt across projects. It provides specific methodologies, tools, and indicators for identifying technical debt at various levels of sophistication.

Assessment Principles

Cross-Language Applicability

  • Assessment should work across different programming languages
  • Technology-agnostic core metrics
  • Language-specific adaptations where necessary
  • Support for polyglot systems

Easy to Understand

  • Clear, actionable metrics
  • Simple scoring systems
  • Visual representations of debt
  • Non-technical stakeholder communication

Customizable Thresholds

  • Adjustable based on project needs
  • Team-specific configurations
  • Domain-appropriate measurements
  • Progressive strictness options

Non-Redundant Checks

  • Avoid overlapping metrics
  • Focus on distinct aspects of quality
  • Prevent metric gaming
  • Balance opposing concerns

Debt Assessment Levels

ROOKIE Level: Static Code Analysis

Core Static Metrics

  • Complexity metrics
    • Cyclomatic complexity
    • Cognitive complexity
    • Maintainability index
  • Size indicators
    • Method length
    • File length
    • Argument count
    • Method count
  • Duplication detection
    • Identical code blocks
    • Similar code blocks
  • Control flow analysis
    • Nested control structures
    • Return statements
    • Boolean logic complexity

Interpretation Guidelines

  • Establish baseline measurements
  • Set appropriate thresholds
  • Compare against historical data
  • Identify outliers and hotspots

EXPERIENCED Level: Technical Debt Radar

Multi-Dimensional Analysis

  • Code-level concerns
    • Style consistency
    • API design
    • Documentation quality
  • Architectural issues
    • Component coupling
    • Dependency management
    • Architectural drift
  • Test quality
    • Coverage gaps
    • Test reliability
    • Test maintenance cost
  • Operational factors
    • Deployment complexity
    • Configuration management
    • Monitoring coverage

Visualization Techniques

  • Technical debt quadrants
  • Heatmaps of problematic areas
  • Trend visualization
  • Risk vs. remediation effort matrices

GURU Level: Behavioral Code Analysis

Code Hotspots Identification

  • Change frequency analysis
    • Files changed most often
    • Unstable interfaces
    • Frequently patched components
  • Change coupling
    • Files that change together
    • Hidden dependencies
    • Cross-module impacts
  • Developer effort
    • Time spent on maintenance
    • Refactoring attempts
    • Expert knowledge requirements
  • Issue correlation
    • Bug-prone areas
    • Performance bottlenecks
    • Security vulnerability patterns

Code Biomarkers

  • Growth patterns
    • Unhealthy expansion rates
    • Feature creep indicators
    • Complexity acceleration
  • Semantic indicators
    • Comment sentiment analysis
    • TODO/FIXME density
    • Dead code accumulation
  • Knowledge distribution
    • Code ownership concentration
    • Tribal knowledge areas
    • Onboarding friction points

10-Point Technical Debt Assessment

Size Checks

  1. Method Length: Functions or methods with excessive lines of code
  2. File Length: Files with excessive total lines of code
  3. Argument Count: Methods defined with high number of arguments
  4. Method Count: Classes with high number of methods/functions

Control Flow Checks

  1. Return Statements: High number of return statements in a function
  2. Nested Control Flow: Deeply nested control structures (if/case)

Complexity Checks

  1. Complex Boolean Logic: Boolean expressions with many operators
  2. Method Complexity: Functions with high cognitive complexity

Duplication Checks

  1. Identical Blocks: Syntactically identical code across the codebase
  2. Similar Blocks: Structurally similar code with minor variations

Assessment Tools

Code Analysis Tools

  • SonarQube for comprehensive static analysis
  • ESLint/TSLint for JavaScript/TypeScript
  • Pylint/Flake8 for Python
  • CodeClimate Quality for multi-language assessment
  • JaCoCo for Java coverage analysis

Visualization Tools

  • Grafana dashboards for metrics visualization
  • Custom reporting for trend analysis
  • Heat maps for hotspot identification
  • CodeScene for behavioral code analysis

Integration Methods

  • CI/CD pipeline integration
  • Pull request analysis
  • Scheduled comprehensive assessments
  • Pre-release quality gates

Remediation Time Estimation

Estimation Methodology

  • Categorization of issue types
  • Standard time units per issue type
  • Complexity factors
  • Dependency considerations
  • Knowledge requirements

Rating System

  • A-F letter grade scale for files
  • Technical debt ratio calculation
  • Implementation time estimation
  • Comparison across projects

Related Documentation

References