Stop Writing Clean Code. Start Writing Clear Code

Yes, this is another article about clean code. But according to various sources, the ratio of time spent reading versus writing code can reach 7 to 1 — and even more. When you fix a bug, add new functionality, or refactor, you first immerse yourself in logic written by other people (or by yourself several months ago). This is precisely why code readability is a more important factor than the speed of initial writing. Unreadable code is technical debt that slows down the entire team and increases development costs in the long run.

It is important to understand that clean code is not a panacea and not a set of strict, unbreakable laws. Perfect code does not exist. It is more of a philosophy and a set of recommendations that help make code more understandable and maintainable.

The best code is stupidly readable code.

Code that does not force you to strain to understand its intent.

Three Core Principles of Readable Code

1. Meaningful names. Names should reveal intent. If you see variables like d or list1, you will need to scroll through the code to figure out what they mean. But elapsedTimeInDays or activeUsers are self-explanatory. Code should read like prose.

2. Single Responsibility Principle (SRP). Functions should do one thing and do it well. A function called handleUserData that retrieves form data, validates it, saves it to a database, and sends an email is a red flag. Break it into separate functions: validateForm, saveUserToDatabase, sendWelcomeEmail.

3. Consistent code style. It is not critical whether you use tabs or spaces, or where you place braces. What matters is that the entire team does it the same way. Inconsistent style creates visual noise. Use linters and formatters to solve this automatically.

Modern Code Review Approaches

Code review is not about finding culprits — it is one of the most effective tools for collective growth and improving product quality.

A review checklist should cover:

• Logic: Does the code solve the task? Are edge cases handled?
• Readability: Are names clear? Is the structure too complex? Could a new team member understand this code in a month?
• Architecture: Does the code violate project patterns? Does it create workarounds?
• Tests: Are tests present? Do they cover main and alternative scenarios?

Let robots work first. Use tools like SonarQube and CodeClimate in CI/CD pipelines to catch 90% of typical problems before human review.

Focus on teaching, not criticism. Instead of "Fix this," try: "What if we tried this approach? I think it would simplify maintenance" — or — "I do not fully understand this part; can you explain why this solution was chosen?"

Patterns and Anti-patterns: Explained with Cats

Deep Nesting (Arrow Code)

Consider this anti-pattern — a pyramid of indentation that forces you to hold an entire chain of conditions in your head:

if (cat wants to play) {
  if (it is in the living room) {
    if (there is a blanket on the sofa) {
      if (there is a box under the blanket) {
        if (there is catnip in the box) {
          // Hooray, the cat is high!
        }
      }
    }
  }
}

The better approach is Guard Clauses — check all negative scenarios immediately and exit early:

if (cat does NOT want to play) {
  // exit — mission cancelled
}
if (it is NOT in the living room) {
  // exit — it is busy elsewhere
}
if (there is NO blanket on the sofa) {
  // exit — nothing to search under
}
if (there is NO box under the blanket) {
  // exit — no hiding spot
}
if (there is NO catnip in the box) {
  // exit — target not found
}
// Hooray, the cat is high!

Code becomes flat, clean, and understandable.

Magic Numbers

What do 1, 2, and 3 mean here?

if (catState === 1) {
  // Pet
} else if (catState === 2) {
  // Feed
} else if (catState === 3) {
  // Do not touch — cat is dead
}

You remember today that 2 means "hungry," but in a month you will need to search the documentation. Use named constants instead:

const STATE_PURRING = 1;
const STATE_HUNGRY  = 2;
const STATE_DEAD    = 3;

if (catState === STATE_PURRING) {
  // Pet
} else if (catState === STATE_HUNGRY) {
  // Feed
} else if (catState === STATE_DEAD) {
  // Do not touch — cat is dead
}

Named constants serve as living documentation. If the value for a hungry cat changes from 2 to 5, you only update it in one place.

Copy-Paste Code (DRY Violation)

If you decide to add vitamins to the cats' diet, you must change the logic in two places. With ten cats, you risk forgetting one — a guaranteed bug:

// Feed Barsik
putInBowl("Barsik", "dry food");
pourInBowl("Barsik", "water");
callCat("Barsik");

// Feed Murzik
putInBowl("Murzik", "dry food");
pourInBowl("Murzik", "water");
callCat("Murzik");

Extract the logic into a reusable function (the DRY principle):

function feedCat(catName) {
  putInBowl(catName, "dry food");
  pourInBowl(catName, "water");
  callCat(catName);
}

feedCat("Barsik");
feedCat("Murzik");

All feeding logic is now in one place. Changes to one function affect all call sites. Code is shorter, cleaner, and more reliable.

Readable Code and Documentation

Good code strives to be self-documenting. Its structure and naming are so clear that comments explaining what it does become unnecessary. However, comments are still needed to explain why code is written a certain way — for example, if you used a strange workaround for a bug in an external library, leave a comment with a link to the issue.

For documenting public APIs, use tools that generate documentation from code: JSDoc, Sphinx, or Swagger/OpenAPI.

Practical Tips for Building a Readable-Code Culture

• Regular mini-refactorings. See a poorly named variable? Rename it. Notice a long function? Break it into two. Leave code slightly cleaner than you found it. This is a culture of small, constant improvements.
• Pair programming. This is essentially real-time code review. One person writes, the other watches, asks questions, and suggests ideas — a great opportunity to exchange knowledge and produce cleaner code immediately.
• Create internal style guides. Agree on common rules within your team and document them. This resolves most code review disputes and establishes a unified standard.

Conclusion: Pragmatism Above All

Readable code accelerates development in the long term. That is a fact. But clean code is not a panacea, and perfect code does not exist.

Do not blindly follow complex patterns if the task does not require them. If you are writing a one-time data migration script or a prototype you will discard tomorrow, spending hours on perfect architecture is counterproductive. Context decides everything.

The worst thing you can do is mindlessly apply complex patterns and principles where a simple solution suffices. Three weeks later you have a monster that takes longer to spin up than it runs. It is complex, impossible to maintain, and solves a problem that never really existed.

The goal is not to write clean code for cleanliness's sake, but to write maintainable code where it truly matters. Seek balance, think about your project's future, and always ask yourself: will this added complexity make the next developer's life — possibly your own — easier or harder?