Visualize codebase size growth

Tracking and visualizing codebase size growth is a key practice for software development teams aiming to understand project evolution, detect potential risks, and plan resources effectively. Here’s a comprehensive guide on how to visualize codebase size growth:

Key Metrics to Measure Codebase Size Growth

1. Lines of Code (LOC)
   The most common metric; counts the total number of lines in the codebase over time.

2. Number of Files
   Tracking the number of source code files can indicate structural complexity growth.

3. Number of Commits
   Reflects activity but indirectly related to size.

4. Codebase Size in Kilobytes/Megabytes
   Total disk size occupied by the source code.

5. Function/Method Count
   More granular insight into complexity and growth.

6. Dependency Count
   Growth of external libraries and packages used.

Data Collection Methods

• Git Log and Scripts
  Use Git history to track changes in LOC or file count over time.

  Example command to get LOC per commit date:

  bash
  CopyEdit
  git log --pretty=format:"%ci" --reverse | while read date; do
      loc=$(git checkout $date --quiet && find . -name '*.js' -o -name '*.py' | xargs wc -l | tail -1 | awk '{print $1}');
      echo "$date $loc";
  done
  

• Static Analysis Tools
  Tools like cloc can count lines by language and provide detailed breakdowns.

• CI/CD Integration
  Automate metrics extraction during builds and store them for visualization.

Visualization Techniques

1. Time Series Line Charts
   Plot LOC or file count over time, showing growth trends, plateaus, or reductions.

   • X-axis: Time (days, weeks, months)

   • Y-axis: LOC / number of files

2. Stacked Area Charts
   Show growth by component, language, or module to highlight where the growth is concentrated.

3. Heatmaps
   Highlight periods with unusual spikes or drops in size or complexity.

4. Cumulative Commit Growth
   Visualize how the number of commits or contributors correlates with size.

Example Using Python and Matplotlib

python
CopyEdit
import matplotlib.pyplot as plt
import pandas as pd

# Example data: dates and LOC counts
data = {
    'date': pd.date_range(start='2023-01-01', periods=12, freq='M'),
    'loc': [1200, 1350, 1500, 1700, 2100, 2300, 2600, 2900, 3100, 3400, 3600, 4000]
}

df = pd.DataFrame(data)
plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['loc'], marker='o', linestyle='-', color='b')
plt.title('Codebase Size Growth Over Time')
plt.xlabel('Date')
plt.ylabel('Lines of Code')
plt.grid(True)
plt.show()

Best Practices

• Regular Monitoring: Track size at regular intervals (daily, weekly).

• Correlate Growth With Features: Map growth spikes to feature development or refactoring.

• Set Alerts for Sudden Changes: Sudden large increases or decreases may signal technical debt or risky changes.

• Visualize by Module: Break down size by subprojects to pinpoint growth areas.

Visualizing codebase size growth helps maintain project health, guides technical debt management, and supports better decision-making throughout the software lifecycle.