Web App Complexity & Security Risks

TL;DR

More complex web applications generally have more vulnerabilities. We can measure this complexity using metrics like lines of code, cyclomatic complexity, and the number of dependencies. Regularly assessing these metrics helps identify areas needing extra security attention.

Understanding the Link

It’s a simple idea: the bigger and more complicated something is, the more places there are for things to go wrong. Web applications are no different. More code means more potential bugs, and more external libraries mean more potential supply chain risks.

Measuring Complexity – A Step-by-Step Guide

1. Lines of Code (LOC): This is the most basic measure.
   • It’s easy to calculate, but doesn’t tell you much about *how* complex the code is.
   • Tools like wc -l on Linux/macOS can help:

     wc -l *.py  # Counts lines in all Python files

2. Cyclomatic Complexity: This measures the number of independent paths through your code. Higher complexity means more testing is needed.
   • A function with a simple if/else has low cyclomatic complexity. A nested series of if/else statements, or lots of loops, will have higher complexity.
   • Python Example (using Radon): Install with pip install radon then run:

     radon cc your_file.py

   • JavaScript Example (using ESLint with a plugin): Install ESLint and the complexity plugin, then configure it in your .eslintrc.js file to report cyclomatic complexity violations.

     npm install eslint eslint-plugin-complexity --save-dev

3. Dependency Count: External libraries add functionality, but also risk.
   • Each dependency is a potential entry point for vulnerabilities.
   • Node.js Example (using npm):

     npm ls --depth=0

     This lists your direct dependencies. Consider using tools like npm audit to identify known security issues in these dependencies.

   • Python Example (using pipreqs): Install with pip install pipreqs then run:

     pipreqs /path/to/your/project

     This generates a requirements file listing your project’s dependencies.

4. Code Churn: How often code is changed.
   • Frequently changing code is more likely to contain bugs, as it hasn’t been thoroughly tested.
   • Use version control history (e.g., git log) to identify files with high churn rates.

     git log --author="*" --pretty=format:%an - %ae - %ad - %s | sort | uniq -c | sort -nr

5. Static Analysis Reports: Tools like SonarQube, Veracode or Snyk provide comprehensive complexity and security analysis.
   • These tools often combine multiple metrics (LOC, cyclomatic complexity, dependency vulnerabilities) into a single risk score.

Putting it all Together

1. Regularly Measure: Automate these measurements as part of your build process.
2. Set Thresholds: Define acceptable levels for each metric (e.g., maximum cyclomatic complexity per function).
3. Prioritize Reviews: Focus security reviews on code with high complexity, churn, or dependency vulnerabilities.
4. Dependency Management: Keep dependencies up-to-date and use tools to scan for known issues.