Dependency Dilemma: 3 Hidden Costs of Over-Installing

✨ AI Summary: Are you over-relying on `npm install`? This Clean Code reality check uncovers the hidden costs of excessive development dependencies, from bloated bundle sizes and security risks to maintenance headaches. Discover why a simple DIY approach for common tasks often delivers better performance and a more maintainable codebase than importing heavy libraries.
Lessy Kia Lessy Kia
January 1, 2026
Dependency Dilemma: 3 Hidden Costs of Over-Installing

The "Clean Code" Reality Check: When DIY is Better Than a Library

In the modern development ecosystem, our first instinct when facing a problem is often to reach for the terminal and type npm install or pip install. While the open-source world is a developer's superpower, this "dependency-first" mindset has a hidden cost. Sometimes, the cleanest code isn't the package you imported—it's the ten lines you wrote yourself.

The Hidden Cost of the "Quick Fix"

Adding a library might save you thirty minutes today, but it introduces three invisible burdens that can haunt your project for years:

1. The "Kitchen Sink" Problem

Many libraries are built to be all things to all people. If you only need a single function to format a date or capitalize a string, importing a 500KB library like Moment.js or Lodash is overkill. You are forcing your users to download code that will never be executed.

2. Maintenance and Security Debt

Every dependency is a liability. By adding a library, you are now responsible for:

  • Monitoring it for security vulnerabilities (CVEs).
  • Handling breaking changes when the library updates.
  • Risking project failure if the maintainer decides to delete the repository (the "Left-Pad" incident).

3. The "Black Box" Bug

When a bug occurs inside your own code, you can fix it immediately. When a bug occurs inside a deep dependency, you spend hours digging through GitHub issues and waiting for a maintainer to merge a pull request.


A Practical Example: Array Shuffling

Let's look at a common task: shuffling an array for a UI element.

The Dependency Approach

Many developers would pull in a heavy utility library just for a shuffle method.

  • Cost: Increases bundle size, adds build-time overhead, and introduces external risk.

The DIY Approach (Fisher-Yates Shuffle)

You can solve this with a clean, standard algorithm in plain JavaScript:

/**
 * Shuffles an array in-place using the Fisher-Yates algorithm.
 * Time Complexity: O(n)
 */
function shuffle(array) {
  for (let i = array.length - 1; i > 0; i--) {
    const j = Math.floor(Math.random() * (i + 1));
    [array[i], array[j]] = [array[j], array[i]];
  }
  return array;
}

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