Writing code involves more than just making it functional, it focuses on creating solutions that are understandable, maintainable, and flexible. Yet, as deadlines approach and requirements change, our codebases frequently reveal subtle indicators that they may not be as robust as they appear. These indicators are commonly referred to as code smells and anti-patterns. In this blog today, we will explore the meanings of these terms, their significance, and how software developers can begin to identify and tackle them in their own projects.
What Are Code Smells vs Anti-Patterns?
A code smell is like a little red flag. It doesn’t necessarily mean your code is broken, but something might be off. Think of it as an indicator of hidden trouble.
An anti-pattern is a commonly used approach or structure that seems reasonable but tends to lead to problems. It’s like following the “wrong recipe” because it looks familiar.
In short, we shall term code smells are symptoms and anti-patterns are traps. Recognizing both helps keep your codebase healthy, especially as your projects grow or you work with others
Five Common Code Smells
Here are five code smells to be aware of, along with simple examples:
1. Duplicated Code
If you notice the same logic appearing in multiple locations, that’s a code smell. For example
def calculate_area_rectangle(length, width):
return length * width
def calculate_area_square(side):
return side * side
Here, calculate_area_square merely duplicates logic. Duplicated code complicates maintenance if a bug is present in one instance, it is likely present in others as well.
2. Large Class / Method
When a class or method attempts to handle too many responsibilities, it becomes difficult to comprehend, test, or maintain. For example, a User class that also manages discount calculations breaches the single responsibility principle. Instead, that functionality could be placed in a separate DiscountCalculator.
3. Long Parameter List
These are methods that require numerous parameters are harder to read and invoke correctly. For example:
def create_user(name, age, address, phone, email, gender, occupation):
pass
Organizing related parameters or encapsulating them within an object can simplify the process.
4. Feature Envy
When a method in one class predominantly interacts with the data of another class, the logic likely belongs in the latter. For example, a get_full_address method in the User class that extensively accesses data from the Address class should probably reside in the Address class.
5. Data Clumps
This refers to a collection of variables that consistently appear together such as street, city, state, postal code and indicates a lack of abstraction. Instead, they should be grouped into an Address class or struct. Having ungrouped data results in redundancy and inconsistencies.
Common Anti-Patterns to Avoid
Here are several prevalent anti-patterns and the reasons they pose risks:
1. Golden Hammer
Dependence on a familiar tool due to personal preference, even when it is inappropriate for the task at hand. For instance, utilizing a list comprehension for side effects such as printing in Python solely because of a fondness for list comprehensions.
2. Cargo Cult Programming
Imitating structures, patterns, or frameworks that you have observed without comprehending their purpose or applicability. For example, incorporating a decorator in Python that serves no significant function merely because other code examples included decorators.
3. Analysis Paralysis
Allocating excessive time to planning, resulting in no actual progress. While planning is beneficial, there comes a time when one must construct, test, and iterate. Over-analysis can hinder advancement.
4. God Object
A class or module that encompasses all functionalities—managing data, processing, displaying, logging, etc. This centralization undermines modularity and increases the risk associated with changes. An example would be a SystemControl class that logs errors, saves data, processes data, displays data, and so forth.
5. Spaghetti Code
Code lacking a clear structure or modularity, characterized by numerous nested loops and conditionals. This complexity makes debugging or extending the code exceedingly challenging. An example includes deeply nested if statements and loops within a single function.
Here’s why you should care as a aspiring developer or even as someone interested in code;
When you write code applying these ideas makes your work cleaner, more maintainable, and often higher quality.
When you show up for internships or team projects, knowing about code smells and anti-patterns gives you a professional edge,you’ll write code that is easier for others to work with. If you eventually lead or participate in code reviews, you’ll be able to spot and explain refactoring opportunities and not just “it works”, but “it works and is maintainable”. As your projects grow, technical debt can bite hard. Early awareness helps you avoid getting overwhelmed by messy code in bigger projects.
Conclusion
Recognizing code smells and anti-patterns isn’t about perfection, it’s about awareness and intentional improvement. By spotting the subtle indicators, you give your codebase and yourself a chance to evolve gracefully rather than crumble under its own complexity. If you want to take a next step: pick one small project maybe an assignment, or one module of your Android app and identify one smell you can fix. Refactor it. See how your code feels afterwards. Because when you clean up smells and avoid traps, your code becomes more than just a working program it becomes something you’re proud of.
References:
https://blog.codacy.com/code-smells-and-anti-patterns
From the blog CS@Worcester – MY_BLOG_ by Serah Matovu and used with permission of the author. All other rights reserved by the author.
