What’s the Big Deal About Code Smells?
You’ve probably heard the term code smell thrown around in tech chats or developer forums. It sounds weird, but it’s pretty simple. A code smell is a sign that something might be wrong with your code’s structure, even if it still works. It’s not a bug – it’s more like a warning.
In 2025, we’re using smarter tools. AI pair programmers, auto-suggestions, and one-click fixes are everywhere. But even with all this help, messy code still creeps in. Why? Because writing fast isn’t the same as writing well.
Let’s walk through what code smells are, which ones still show up often, and how to clean them up – with or without AI.
What Exactly Is a Code Smell?
The term was first popularized by Martin Fowler and Kent Beck. A code smell is a surface-level symptom in your code that might indicate a deeper problem. Think of it like a weird noise in your car – it may still drive fine, but something under the hood needs attention.
Common smells include:
Repeated code
Long methods
Too many parameters
Large classes doing too much
Feature envy (when one class depends too heavily on another)
Smells don’t mean “broken”, but they often lead to bugs, poor performance, and burnout when the code becomes hard to maintain.
Why Code Smells Still Matter in 2025
You might think modern AI tools would fix these for us – and yes, they help. But they can also make things worse if we’re not careful.
Here’s why code smells still matter:
Fast isn’t always better: Copilot or other tools can suggest working code quickly, but if you don’t pause to refactor, you build technical debt faster than ever.
Maintenance still matters: Most devs spend more time reading code than writing it. Clean, smell-free code saves time for your future self (and teammates).
Team collaboration: Smelly code makes onboarding hard. New devs get lost in unclear logic or bloated files.
5 Common Code Smells (and How to Fix Them Today)
Let’s look at five code smells you might see in 2025 – and how to handle them with a mix of good habits and modern tools.
1. Long Functions
The problem: A function that’s 80+ lines long is hard to test, reuse, or understand.
Fix it: Break it into smaller, single-purpose functions. Use descriptive names to show what each step does.
Modern help: Use IDEs like VSCode or JetBrains to extract functions. Tools like Copilot can suggest shorter versions if you give a clear prompt like “split into smaller functions.”
2. Duplicate Code
The problem: Copy-pasting the same logic across files leads to bugs when only one copy gets updated.
Fix it: Create utility functions or shared components. If the logic repeats, it probably belongs in one place.
Modern help: GitHub Copilot and CodeWhisperer can help detect duplication. Refactor tools in your IDE can suggest ways to DRY (Don’t Repeat Yourself) your code.
3. Large Classes
The problem: Classes that do too much violate the Single Responsibility Principle. They’re hard to test and even harder to debug.
Fix it: Split the class into smaller ones based on roles. For example, separate data logic from UI logic.
Modern help: AI tools won’t always suggest splitting classes, but they can help you rewrite chunks once you decide what belongs where.
4. Too Many Parameters
The problem: Functions with five or more parameters are hard to follow and easy to mess up.
Fix it: Use objects or configuration structs. Group related parameters together.
Modern help: Many IDEs highlight functions with too many arguments. Chat-based tools like ChatGPT or Cody by Sourcegraph can help restructure function signatures.
5. Feature Envy
The problem: When one class constantly accesses data or methods from another, it may mean poor separation of concerns.
Fix it: Move behavior closer to the data. If Class A is always asking Class B for data, maybe that behavior belongs in Class B.
Modern help: AI code review tools can flag this during pull requests. But good judgment matters most here.
How AI Helps – and Where It Doesn’t
In 2025, you have smart assistants everywhere. GitHub Copilot, Amazon CodeWhisperer, ChatGPT Code Interpreter, etc. They’re great at:
Spotting repeated patterns
Auto-suggesting refactors
Reviewing PRs quickly
But they don’t understand your project like you do. They won’t always know your team’s coding standards, future plans, or what’s easiest to maintain long-term.
So use AI like a helpful junior dev — one who’s fast but needs guidance.
Quick Refactoring Checklist (2025 Edition)
Before you commit code, ask yourself:
Can I read this function in one breath?
Is there any repeated logic I can reuse?
Are my class responsibilities clear?
Can someone new understand this without help?
Did I rely too much on AI without thinking?
Conclusion: Clean Code Still Wins
Even in 2025, code smells haven’t gone out of style. If anything, they’re more important — because code is being written faster than ever. With AI tools by your side and a bit of discipline, you can write clean, clear, and future-proof code.
Don’t just chase working code. Aim for understandable code. Your future self (and your team) will thank you.
