# What is Software Entropy?

## Definition

Software entropy is a measure of the disorder, complexity, and degradation in a software system. The concept is borrowed from thermodynamics, where entropy measures the disorder in a physical system.

## The Second Law of Software Dynamics

Just as the second law of thermodynamics states that entropy in a closed system always increases, software systems tend toward disorder over time unless energy is actively applied to maintain order. This manifests as:

- **Code Rot**: Gradual degradation of code quality
- **Technical Debt**: Accumulated shortcuts and suboptimal solutions
- **Complexity Creep**: Increasing difficulty to understand and modify code
- **Dependency Hell**: Tangled web of interconnected components

## Components of Software Entropy

### 1. Structural Complexity
- Cyclomatic complexity (decision paths)
- Nesting depth
- Class/function coupling
- Module interdependencies

### 2. Size and Scale
- Lines of code
- Number of files
- Directory depth
- Function/class size

### 3. Duplication
- Copy-pasted code
- Similar patterns
- Repeated logic

### 4. Documentation Decay
- Missing docstrings
- Outdated comments
- Unclear naming

### 5. Change Volatility
- Frequent modifications
- Hot spots (files that change often)
- Code churn

### 6. Dependency Chaos
- External dependencies
- Version conflicts
- Circular dependencies

## Mathematical Model

swent uses a weighted entropy formula:

$$E_{total} = \sum_{i=1}^{n} w_i \cdot E_i$$

Where:
- $E_{total}$ is the total entropy (0-1)
- $w_i$ is the weight for component $i$
- $E_i$ is the normalized entropy for component $i$
- $\sum w_i = 1.0$

## Entropy Growth Factors

### Natural Growth
- Feature additions
- Bug fixes
- Requirement changes
- Team changes

### Accelerating Factors
- Rushed deadlines
- Lack of refactoring
- Poor initial design
- Inadequate testing

### Mitigating Factors
- Regular refactoring
- Code reviews
- Automated testing
- Documentation standards

## The Cost of High Entropy

Research shows that high software entropy leads to:

- **50-75%** more time for new features
- **3x** more bugs per change
- **10x** higher onboarding time
- **Exponential** increase in maintenance cost

## Measuring with swent

swent quantifies entropy by:

1. **Analyzing** multiple quality dimensions
2. **Normalizing** each to a common scale
3. **Weighting** based on impact
4. **Combining** into a single score

This provides an objective, reproducible measure of code health that can be tracked over time and compared across projects.