cargo-coupling

Measure the "right distance" in your Rust code.

cargo-coupling analyzes coupling in Rust projects based on Vlad Khononov's "Balancing Coupling in Software Design" framework. It calculates a Balance Score from three core dimensions: Integration Strength, Distance, and Volatility.

⚠️ Experimental Project

This tool is currently experimental. The scoring algorithms, thresholds, and detected patterns are subject to change based on real-world feedback.

We want your input! If you try this tool on your project, please share your experience:

• Are the grades and scores meaningful for your codebase?
• Are there false positives or patterns that shouldn't be flagged?
• What additional metrics would be useful?

Please open an issue at GitHub Issues to discuss. Your feedback helps improve the tool for everyone.

Quick Start

1. Install

cargo install cargo-coupling

Or use Docker:

docker pull ghcr.io/nwiizo/cargo-coupling

2. Analyze

# Analyze current project (default: shows only important issues)
cargo coupling ./src

# Show summary only
cargo coupling --summary ./src

# Japanese output with explanations (日本語出力)
cargo coupling --summary --japanese ./src
cargo coupling --summary --jp ./src

# Show all issues including Low severity
cargo coupling --summary --all ./src

3. Refactor with AI

# Generate AI-friendly output
cargo coupling --ai ./src

Copy the output and use this prompt with Claude, Copilot, or any AI coding assistant:

The following is the output of `cargo coupling --ai`, which analyzes coupling issues in a Rust project.
For each issue, suggest specific code changes to reduce coupling.
Focus on introducing traits, moving code closer, or breaking circular dependencies.

Example output:

Coupling Issues in my-project:
────────────────────────────────────────────────────────────

Grade: B (Good) | Score: 0.88 | Issues: 0 High, 5 Medium

Issues:

1. 🟡 api::handler → db::internal::Query
   Type: Global Complexity
   Problem: Intrusive coupling to db::internal::Query across module boundary
   Fix: Introduce trait `QueryTrait` with methods: // Extract required methods

2. 🟡 25 dependents → core::types
   Type: High Afferent Coupling
   Problem: Module core::types is depended on by 25 other components
   Fix: Introduce trait `TypesInterface` with methods: // Define stable public API

The AI will analyze patterns and suggest specific refactoring strategies.

4. Interactive Web Visualization (Experimental)

⚠️ Experimental Feature: The Web UI is currently in an experimental state. The interface, features, and behavior may change significantly in future versions.

# Start interactive web UI
cargo coupling --web ./src

# Custom port
cargo coupling --web --port 8080 ./src

The web UI provides:

• Interactive graph visualization with Cytoscape.js
• Hotspots panel: Top refactoring targets ranked by severity
• Blast Radius: Impact analysis with risk score
• Clusters: Architecture grouping detection
• Filtering by strength, distance, volatility, balance score
• Source code viewing with syntax highlighting

5. Job-Focused CLI Commands

For quick, focused analysis without opening the web UI:

# Find top refactoring targets
cargo coupling --hotspots ./src
cargo coupling --hotspots=10 ./src

# With beginner-friendly explanations
cargo coupling --hotspots --verbose ./src

# Analyze change impact for a specific module
cargo coupling --impact main ./src
cargo coupling --impact analyzer ./src

# Trace dependencies for a specific function or type
cargo coupling --trace analyze_file ./src
cargo coupling --trace BalanceScore ./src

# CI/CD quality gate (exits with code 1 on failure)
cargo coupling --check ./src
cargo coupling --check --min-grade=B ./src
cargo coupling --check --max-critical=0 --max-circular=0 ./src

# Machine-readable JSON output
cargo coupling --json ./src
cargo coupling --json ./src | jq '.hotspots[0]'

Example --hotspots --verbose output:

#1 my-project::main (Score: 55)
   🟡 Medium: High Efferent Coupling

   💡 What it means:
      This module depends on too many other modules

   ⚠️  Why it's a problem:
      • Changes elsewhere may break this module
      • Testing requires many mocks/stubs
      • Hard to understand in isolation

   🔧 How to fix:
      Split into smaller modules with clear responsibilities
      e.g., Split main.rs into cli.rs, config.rs, runner.rs

More Options

# Generate detailed report to file
cargo coupling -o report.md ./src

# Show timing information
cargo coupling --summary --timing ./src

# Use 4 threads for parallel processing
cargo coupling -j 4 ./src

# Skip Git history analysis for faster results
cargo coupling --no-git ./src

Features

• 3-Dimensional Balance Score: Calculates coupling balance based on Integration Strength, Distance, and Volatility (0.0 - 1.0)
• Khononov Balance Formula: BALANCE = (STRENGTH XOR DISTANCE) OR NOT VOLATILITY
• Interactive Web UI: --web flag starts a browser-based visualization with graph, hotspots, and blast radius analysis
• Job-Focused CLI: Quick commands for common tasks (--hotspots, --impact, --check, --json)
• Japanese Support: --japanese / --jp flag for Japanese output with explanations and design decision matrix
• Noise Reduction: Default strict mode hides Low severity issues (--all to show all)
• Beginner-Friendly: --verbose flag explains issues in plain language with fix examples
• CI/CD Quality Gate: --check command with configurable thresholds and exit codes
• AI-Friendly Output: --ai flag generates output optimized for coding agents (Claude, Copilot, etc.)
• Rust Pattern Detection: Detects newtype usage, serde derives, public fields, primitive obsession
• Issue Detection: Automatically identifies problematic coupling patterns (God Module, etc.)
• Circular Dependency Detection: Detects and reports dependency cycles
• Visibility Tracking: Analyzes Rust visibility modifiers (pub, pub(crate), etc.)
• Git Integration: Analyzes change frequency from Git history for volatility scoring
• Configuration File: Supports .coupling.toml for volatility overrides
• Parallel Processing: Uses Rayon for fast analysis of large codebases
• Configurable Thresholds: Customize dependency limits via CLI or config
• Markdown Reports: Generates detailed analysis reports
• Cargo Integration: Works as a cargo subcommand

Khononov's Coupling Balance

Coupling Balance is a framework proposed by Vlad Khononov that evaluates coupling between modules across three dimensions to guide design decisions.

Core Principle

Coupling is not inherently bad. What matters is the balance between coupling strength, distance, and volatility.

The Three Dimensions

1. Strength (Integration Strength)

Represents how tightly components depend on each other.

Level	Description	Rust Example	Score
Intrusive	Direct dependency on internal implementation	Direct access to struct.field	1.00 (strong)
Functional	Dependency on behavior	Method calls on concrete types	0.75
Model	Dependency on data structures	Sharing type definitions	0.50
Contract	Dependency on interfaces only	Access via trait	0.25 (weak)

→ Lower in the table = weaker coupling (preferred)

2. Distance

The physical or logical distance between dependent components.

Level	Description	Score
Same Module	Within the same module	0.25 (close)
Different Module	Different module in the same crate	0.50
External Crate	Dependency on external crate	1.00 (far)

→ Lower in the table = farther distance

3. Volatility

How frequently a component changes (automatically calculated from Git history).

Level	Description	Changes (6 months)	Score
Low	Stable, rarely changes	0-2 times	0.00
Medium	Occasionally changes	3-10 times	0.50
High	Frequently changes	11+ times	1.00

Note: Volatility requires Git history. Use cargo coupling ./src (not --no-git) to enable volatility analysis.

The Balance Law

Good design follows this principle:

Strong coupling is only acceptable when distance is close OR volatility is low

Expressed as a logical formula:

BALANCED = (STRENGTH ≤ threshold) OR (DISTANCE = near) OR (VOLATILITY = low)

Or Khononov's formula:

BALANCE = (STRENGTH XOR DISTANCE) OR NOT VOLATILITY

• STRENGTH XOR DISTANCE: Strong coupling × close distance OR weak coupling × far distance = Good
• OR NOT VOLATILITY: Even if the above isn't satisfied, low volatility makes it acceptable

Design Decision Matrix

Strength	Distance	Volatility	Decision	Reason
Strong	Close	Low-Medium	✅ OK	High cohesion, changes are localized
Weak	Far	Any	✅ OK	Loose coupling with healthy dependencies
Strong	Far	Any	⚠️ Needs improvement	Change impact spreads widely (global complexity)
Strong	Any	High	⚠️ Needs improvement	Changes cascade through the system
Weak	Close	Low	🤔 Consider	Opportunity for integration (possibly over-modularized)

Improvement Patterns

Pattern 1: Reducing Coupling Strength via Abstraction

Problem: Strong coupling + far distance

┌─────────────┐         ┌─────────────┐
│  Module A   │ ──────▶ │  Module B   │
│             │  strong  │  (impl)     │
└─────────────┘         └─────────────┘
       far distance (different module)

Solution: Introduce a Contract (trait)

┌─────────────┐         ┌─────────────┐
│  Module A   │ ──────▶ │   trait T   │
│             │   weak   │ (contract)  │
└─────────────┘         └─────────────┘
                              ▲
                              │ implements
                        ┌─────────────┐
                        │  Module B   │
                        │   (impl)    │
                        └─────────────┘

Pattern 2: Isolating Volatility

Problem: Strong coupling + high volatility

Solution: Insert a stable interface layer

Concrete Example (Rust)

Before: Problematic Code

// module_a.rs
fn process_user(user: &User) {
    // Direct access to struct internal fields (Intrusive)
    let name = &user.name;           // ← strong coupling
    let age = user.age;              // ← strong coupling
    let email = &user.email_address; // ← breaks if field name changes
    // ...
}

// module_b.rs (frequently modified)
pub struct User {
    pub name: String,
    pub age: u32,
    pub email_address: String,  // ← renamed from email
}

Issues:

• Coupling strength: Intrusive (direct field access)
• Distance: Different Module
• Volatility: High (User struct changes frequently)

After: Improved Code

// contracts.rs (stable layer)
pub trait UserInfo {
    fn display_name(&self) -> &str;
    fn age(&self) -> u32;
    fn contact_email(&self) -> &str;
}

// module_b.rs (implementation details hidden)
pub struct User {
    name: String,        // changed to private
    age: u32,
    email_address: String,
}

impl UserInfo for User {
    fn display_name(&self) -> &str { &self.name }
    fn age(&self) -> u32 { self.age }
    fn contact_email(&self) -> &str { &self.email_address }
}

// module_a.rs (access via trait)
fn process_user(user: &impl UserInfo) {
    let name = user.display_name();    // ← Contract coupling
    let age = user.age();              // ← Contract coupling
    let email = user.contact_email();  // ← unaffected by internal changes
    // ...
}

Improvements:

• Coupling strength: Reduced to Contract (via trait)
• Changes are contained within the User struct
• module_a no longer needs to know User's internal structure

Coupling Balance Summary

Perspective	Guideline
Strong coupling...	Keep components close, or reduce volatility
Far dependencies...	Use weak coupling (Contract)
Highly volatile components...	Isolate with stable abstraction layers

Coupling balance is not about "eliminating coupling" but about "placing the right strength of coupling in the right place."

Numeric Implementation

In the actual implementation:

let alignment = 1.0 - (strength - (1.0 - distance)).abs();
let volatility_impact = 1.0 - (volatility * strength);
let score = alignment * volatility_impact;

CLI Options

cargo coupling [OPTIONS] [PATH]

Arguments:
  [PATH]  Path to analyze [default: ./src]

Options:
  -o, --output <FILE>           Output report to file
  -s, --summary                 Show summary only
      --ai                      AI-friendly output for coding agents
      --all                     Show all issues (default: hide Low severity)
      --japanese, --jp          Japanese output with explanations (日本語)
      --git-months <MONTHS>     Git history period [default: 6]
      --no-git                  Skip Git analysis
  -c, --config <CONFIG>         Config file path (default: .coupling.toml)
  -v, --verbose                 Verbose output with explanations
      --timing                  Show timing information
  -j, --jobs <N>                Number of threads (default: auto)
      --max-deps <N>            Max outgoing dependencies [default: 20]
      --max-dependents <N>      Max incoming dependencies [default: 30]

Web Visualization:
      --web                     Start interactive web UI
      --port <PORT>             Web server port [default: 3000]
      --no-open                 Don't auto-open browser
      --api-endpoint <URL>      API endpoint URL (for separate deployments)

Job-Focused Commands:
      --hotspots[=<N>]          Show top N refactoring targets [default: 5]
      --impact <MODULE>         Analyze change impact for a module
      --trace <ITEM>            Trace dependencies for a function/type
      --check                   CI/CD quality gate (exit code 1 on failure)
      --min-grade <GRADE>       Minimum grade for --check (A/B/C/D/F)
      --max-critical <N>        Max critical issues for --check
      --max-circular <N>        Max circular dependencies for --check
      --fail-on <SEVERITY>      Fail --check on severity (critical/high/medium/low)
      --json                    Output in JSON format

  -h, --help                    Print help
  -V, --version                 Print version

Thresholds

Issue Detection Thresholds

The tool uses the following default thresholds for detecting coupling issues:

Threshold	Default	CLI Flag	Description
Strong Coupling	0.75	-	Minimum strength value considered "strong" (Intrusive level)
Far Distance	0.50	-	Minimum distance value considered "far" (DifferentModule+)
High Volatility	0.75	-	Minimum volatility value considered "high"
Max Dependencies	20	--max-deps	Outgoing dependencies before flagging High Efferent Coupling
Max Dependents	30	--max-dependents	Incoming dependencies before flagging High Afferent Coupling

Health Grade Calculation

Health grades are calculated based on internal couplings only (external crate dependencies are excluded):

Grade	Description	Criteria
S (Over-optimized!)	Stop refactoring!	Medium density <= 5% with >= 20 couplings
A (Well-balanced)	Coupling is appropriate	Medium density 5-10%, no high issues
B (Healthy)	Minor issues, manageable	Medium density > 10%, no critical issues
C (Room for improvement)	Some structural issues	Any high issues OR medium density > 25%
D (Attention needed)	Significant issues	Any critical issues OR high density > 5%
F (Immediate action required)	Critical issues	More than 3 critical issues

Note: S is a WARNING, not a reward. It means you might be over-engineering. Aim for A.

Severity Classification

Issues are classified by severity based on:

Severity	Criteria
Critical	Multiple critical issues detected (circular dependencies, etc.)
High	Count > threshold × 2 (e.g., > 40 dependencies when threshold is 20)
Medium	Count > threshold but <= threshold × 2
Low	Minor issues, generally informational

Output Example

Summary Mode (English)

$ cargo coupling --summary ./src

Balanced Coupling Analysis: my-project
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Grade: B (Good) | Score: 0.67/1.00 | Modules: 14

3-Dimensional Analysis:
  Strength:   Contract 1% / Model 24% / Functional 66% / Intrusive 8%
  Distance:   Same 6% / Different 2% / External 91%
  Volatility: Low 2% / Medium 98% / High 0%

Balance State:
  ✅ High Cohesion (strong+close): 24 (6%)
  ✅ Loose Coupling (weak+far): 5 (1%)
  🤔 Acceptable (strong+far+stable): 352 (92%)

Detected Issues:
  🟡 Medium: 3

Top Priorities:
  - [Medium] metrics → 17 functions, 17 types, 11 impls
  - [Medium] main → 21 dependencies

Summary Mode (Japanese)

$ cargo coupling --summary --jp ./src

カップリング分析: my-project
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

評価: B (Good) | スコア: 0.67/1.00 | モジュール数: 14

3次元分析:
  結合強度: Contract 1% / Model 24% / Functional 66% / Intrusive 8%
           (トレイト)   (型)      (関数)        (内部アクセス)
  距離:     同一モジュール 6% / 別モジュール 2% / 外部 91%
  変更頻度: 低 2% / 中 98% / 高 0%

バランス状態:
  ✅ 高凝集 (強い結合 + 近い距離): 24 (6%) ← 理想的
  ✅ 疎結合 (弱い結合 + 遠い距離): 5 (1%) ← 理想的
  🤔 許容可能 (強い結合 + 遠い距離 + 安定): 352 (92%)

優先的に対処すべき問題:
  - 神モジュール (責務が多すぎる) | metrics
    → モジュールを分割: metrics_core, metrics_helpers

設計判断ガイド (Khononov):
  ✅ 強い結合 + 近い距離 → 高凝集 (理想的)
  ✅ 弱い結合 + 遠い距離 → 疎結合 (理想的)
  🤔 強い結合 + 遠い距離 + 安定 → 許容可能
  ❌ 強い結合 + 遠い距離 + 頻繁に変更 → 要リファクタリング

Coupling Distribution

The tool shows how couplings are distributed by Integration Strength:

By Integration Strength:
| Strength   | Count | %   | Description                    |
|------------|-------|-----|--------------------------------|
| Contract   | 23    | 4%  | Depends on traits/interfaces   |
| Model      | 199   | 31% | Uses data types/structs        |
| Functional | 382   | 59% | Calls specific functions       |
| Intrusive  | 46    | 7%  | Accesses internal details      |

Detected Issues

Critical Severity

• Circular Dependencies: Modules that depend on each other in a cycle

High Severity

• Global Complexity: Strong coupling spanning long distances
• Cascading Change Risk: Strong coupling with frequently changing components

Medium Severity

• God Module: Module with too many functions, types, or implementations
• High Efferent Coupling: Module depends on too many other modules
• High Afferent Coupling: Too many modules depend on this module
• Inappropriate Intimacy: Intrusive coupling across module boundaries

Low Severity (hidden by default, use --all to show)

• Public Field Exposure: Public fields that could use getter methods
• Primitive Obsession: Functions with many primitive parameters (suggest newtype)

Performance

cargo-coupling is optimized for large codebases with parallel AST analysis and streaming Git processing.

Benchmark Results (Large OSS Projects)

Project	Files	With Git	Without Git	Speed
tokio	488	655ms	234ms	745 files/sec
alacritty	83	298ms	161ms	514 files/sec
ripgrep	59	181ms	-	326 files/sec
bat	40	318ms	-	126 files/sec

Performance Features

1. Parallel AST Analysis: Uses Rayon for multi-threaded file processing
2. Optimized Git Analysis: Streaming processing with path filtering
3. Configurable Thread Count: Use -j N to control parallelism

# Show timing information
cargo coupling --timing ./src

# Use 4 threads
cargo coupling -j 4 ./src

# Skip Git analysis for faster results
cargo coupling --no-git ./src

Git Analysis Optimization

The Git volatility analysis is optimized with:

• Path filtering: -- "*.rs" filters at Git level (reduces data transfer)
• Diff filtering: --diff-filter=AMRC skips deleted files
• Streaming: BufReader processes output without loading all into memory
• Async spawn: Starts processing before Git completes

These optimizations provide 5x-47x speedup compared to naive implementation on large repositories.

Library Usage

use cargo_coupling::{
    analyze_workspace,
    generate_report_with_thresholds,
    IssueThresholds,
    VolatilityAnalyzer
};
use std::path::Path;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Analyze project with workspace support
    let mut metrics = analyze_workspace(Path::new("./src"))?;

    // Add volatility from Git history
    let mut volatility = VolatilityAnalyzer::new(6);
    if let Ok(()) = volatility.analyze(Path::new("./src")) {
        metrics.file_changes = volatility.file_changes;
        metrics.update_volatility_from_git();
    }

    // Detect circular dependencies
    let circular = metrics.circular_dependency_summary();
    if circular.total_cycles > 0 {
        println!("Found {} cycles!", circular.total_cycles);
    }

    // Generate report with custom thresholds
    let thresholds = IssueThresholds {
        max_dependencies: 20,
        max_dependents: 25,
        ..Default::default()
    };
    generate_report_with_thresholds(&metrics, &thresholds, &mut std::io::stdout())?;

    Ok(())
}

Docker

Run cargo-coupling without installing Rust:

# Basic analysis
docker run --rm -v $(pwd):/workspace ghcr.io/nwiizo/cargo-coupling coupling /workspace/src

# Summary mode
docker run --rm -v $(pwd):/workspace ghcr.io/nwiizo/cargo-coupling coupling --summary /workspace/src

# Web UI (access at http://localhost:3000)
docker run --rm -p 3000:3000 -v $(pwd):/workspace ghcr.io/nwiizo/cargo-coupling coupling --web --no-open /workspace/src

# Japanese output
docker run --rm -v $(pwd):/workspace ghcr.io/nwiizo/cargo-coupling coupling --summary --jp /workspace/src

Docker Compose

# Run analysis
docker compose run --rm analyze

# Start Web UI
docker compose up web

Available Tags

Tag	Description
latest	Latest release
main	Latest main branch build
vX.Y.Z	Specific version

CI/CD Integration

# .github/workflows/coupling.yml
name: Coupling Analysis

on: [push, pull_request]

jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0  # Full history for volatility analysis

      - name: Install cargo-coupling
        run: cargo install cargo-coupling

      - name: Run coupling analysis
        run: cargo coupling --summary --timing ./src

      - name: Quality gate check
        run: cargo coupling --check --min-grade=C --max-circular=0 ./src

      - name: Generate report
        run: cargo coupling -o coupling-report.md ./src

      - name: Upload report
        uses: actions/upload-artifact@v4
        with:
          name: coupling-report
          path: coupling-report.md

Quality Gate Options

The --check command provides flexible quality gate configuration:

# Fail if grade is below C
cargo coupling --check --min-grade=C ./src

# Fail if there are any circular dependencies
cargo coupling --check --max-circular=0 ./src

# Fail if there are any critical issues
cargo coupling --check --max-critical=0 ./src

# Fail on any high severity or above
cargo coupling --check --fail-on=high ./src

# Combine multiple conditions
cargo coupling --check --min-grade=B --max-circular=0 --max-critical=0 ./src

Exit codes:

• 0: All checks passed
• 1: One or more checks failed

Best Practices

✅ Good: Strong Coupling at Close Distance

mod user_profile {
    pub struct User { /* ... */ }
    pub struct UserProfile { /* ... */ }

    impl User {
        pub fn get_profile(&self) -> &UserProfile { /* ... */ }
    }
}

✅ Good: Weak Coupling at Far Distance

// core/src/lib.rs
pub trait NotificationService {
    fn send(&self, message: &str) -> Result<()>;
}

// adapters/email/src/lib.rs
impl NotificationService for EmailService { /* ... */ }

❌ Bad: Strong Coupling at Far Distance

// src/api/handlers.rs
impl Handler {
    fn handle(&self) {
        // Direct dependency on internal implementation ❌
        let result = database::internal::execute_raw_sql(...);
    }
}

❌ Bad: Circular Dependencies

// module_a.rs
use crate::module_b::TypeB;  // ❌ Creates cycle

// module_b.rs
use crate::module_a::TypeA;  // ❌ Creates cycle

Limitations

This tool is a measurement aid, not an absolute authority on code quality.

Please keep the following limitations in mind:

What This Tool Cannot Do

• Understand Business Context: The tool analyzes structural patterns but cannot understand why certain couplings exist. Some "problematic" patterns may be intentional design decisions.
• Replace Human Judgment: Coupling metrics are heuristics. A high coupling score doesn't always mean bad code, and a low score doesn't guarantee good design.
• Detect All Issues: Static analysis has inherent limitations. Runtime behavior, dynamic dispatch, and macro-generated code may not be fully analyzed.
• Provide Perfect Thresholds: The default thresholds are calibrated for typical Rust projects but may not fit every codebase. Adjust them based on your project's needs.

Important Considerations

• External Dependencies Are Excluded: The health grade only considers internal couplings. Dependencies on external crates (serde, tokio, etc.) are not penalized since you cannot control their design.
• Git History Affects Volatility: If Git history is unavailable or limited, volatility analysis will be incomplete.
• Small Projects May Score Differently: Projects with very few internal couplings (< 10) may receive a Grade B by default, as there's insufficient data for accurate assessment.

Recommended Usage

1. Use as a Starting Point: The tool highlights areas worth investigating, not definitive problems.
2. Combine with Code Review: Human review should validate any suggested refactoring.
3. Track Trends Over Time: Use the tool regularly to track coupling trends rather than focusing on absolute scores.
4. Customize Thresholds: Adjust --max-deps and --max-dependents to match your project's architecture.

The goal is to provide visibility into coupling patterns, empowering developers to make informed decisions.

References

• Vlad Khononov - "Balancing Coupling in Software Design"

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.