10 Common Coding Mistakes And How To Avoid Them

Photo by Antoni Shkraba

When starting coding, it’s natural to make mistakes as we learn the ropes. I’ve encountered several classic errors, which is how I learned to spot and fix them. Whether it’s hardcoding values or overcomplicating solutions, these pitfalls are easy to fall into but equally avoidable. Here are 10 common coding mistakes I’ve seen (and struggled with) as a junior developer and some practical tips I picked up to help us write cleaner, more efficient code.

1. Hardcoding Values

The Mistake: One of the first coding traps is hardcoding values directly into code, whether to specific strings, file paths, or numeric values that change depending on the environment or requirements. Hardcoding creates inflexible code that’s tough to modify when requirements change.

The Fix: Store values in variables, constants, or configuration files. This way, you only need to change them in one place, making maintenance and debugging easier. Environment variables or external configuration files (like .env files in web development) can store values that vary across environments. For example:

# Instead of hardcoding:
tax_rate = 0.07

# Use a constant or config variable:
TAX_RATE = os.getenv('TAX_RATE', default=0.07)

2. Neglecting Error Handling

The Mistake: Error handling is often neglected in early projects. We may assume our code will always work as expected, or we might be in a hurry to complete a task without considering all edge cases. This can lead to unhandled errors that crash our program or make debugging a nightmare.

The Fix: Use try and except blocks (or equivalent error-handling structures in your language) to catch and handle exceptions. It’s also good practice to provide user-friendly error messages and log errors for later troubleshooting. Additionally, checking for edge cases, like empty inputs or invalid data types, can prevent many common errors.

try:
    result = divide(a, b)
except ZeroDivisionError:
    print("Cannot divide by zero.")

3. Writing Overly Complicated Code

The Mistake: Sometimes we feel that complex code means “better” code. I’ve been guilty of this, layering unnecessary logic and trying to find the “perfect” solution. This makes code hard to read, understand, and maintain.

The Fix: Aim for simplicity. Break complex tasks into smaller, manageable functions, each with a single responsibility. Write code that’s as simple as possible and document anything that isn’t immediately obvious. Keeping it simple will make your code cleaner and more robust.

# Instead of multiple nested loops:
for x in list1:
    for y in list2:
        if x == y:
            ...

# Use simpler alternatives where possible, like set intersection:
common_elements = set(list1) & set(list2)

4. Ignoring Code Readability and Comments

The Mistake: Choosing not to write comments or choosing to write unreadable code is a common habit among beginners. Code readability isn’t only about others understanding our code; it’s also about ensuring we can understand it if we revisit it after some time.

The Fix: Follow clean code principles by giving variables descriptive names, using consistent indentation, and writing helpful comments where necessary. Well-placed comments can make a huge difference, especially when explaining complex logic.

# Instead of cryptic variable names:
x = 5

# Use descriptive names:
employee_age = 5  # years

5. Skipping Testing

The Mistake: Skipping testing is a mistake that can have disastrous results. It’s tempting to focus on building features, assuming they work until proven otherwise. This approach leaves code vulnerable to bugs and makes troubleshooting challenging.

The Fix: Writing tests doesn’t need to be daunting. Start small with unit tests for individual functions, ticking each one off as it works as intended. Over time, you can add more extensive testing like integration and regression tests. Testing frameworks – such as PyTest for Python or JUnit for Java – make writing and running tests more manageable.

# Simple unit test for a function
def test_addition():
    assert add(2, 3) == 5

6. Copy-Pasting Code Without Understanding

The Mistake: Don’t rely on copy-pasting code snippets from forums or tutorials when learning. This often leads to a lack of understanding, and errors become harder to fix if we don’t fully grasp the code.

The Fix: Before copying code, take a moment to understand what each line does. Try implementing a similar solution from scratch or breaking down copied code to identify its function. While it may take longer, this approach will deepen your understanding and make you a better coder in the long run.

7. Overusing Global Variables

The Mistake: Using global variables might seem convenient, especially in smaller scripts. However, they can lead to unexpected behavior as multiple components interact with them, making it difficult to debug or control scope.

The Fix: Use local variables whenever possible and minimize the use of global variables. If you need to share data across functions, consider passing it explicitly as function parameters or using classes with instance variables for larger applications.

# Instead of using a global variable:
counter = 0

def increment():
    global counter
    counter += 1

# Use function parameters:
def increment(counter):
    return counter + 1

8. Not Optimizing for Performance Early On

The Mistake: As beginners, we may write code that solves the problem but doesn’t consider efficiency, especially for larger datasets. While some inefficiencies are harmless in small applications, they can quickly become problematic in larger systems.

The Fix: Understand the time complexity of your algorithms and avoid overly complex solutions when simpler ones are available. Studying common data structures and algorithms (such as arrays, hash tables, and binary search) will also help you recognize opportunities for optimization.

# Instead of a slow search through a list:
if element in large_list: ...

# Use a set for faster lookups:
large_set = set(large_list)
if element in large_set:

9. Poor Handling of Null or Empty Values

The Mistake: Many coding errors stem from failing to handle null, empty, or unexpected values. This can lead to crashes or difficult-to-trace bugs.

The Fix: Always validate inputs to your functions or classes. Check for null values or empty inputs and handle them appropriately. Using default values or early return patterns can help prevent issues with null or empty values.

# Instead of assuming a valid input:
def print_length(input_str):
    print(len(input_str))

# Add checks:
def print_length(input_str):
    if input_str is None:
        print("Input cannot be None")
    elif len(input_str) == 0:
        print("Input is empty")
    else:
        print(len(input_str))

10. Failing to Refactor

The Mistake: We often finish a piece of code and move on without revisiting or improving it. Failing to refactor can lead to a build-up of code that’s difficult to maintain and unnecessarily complex.

The Fix: Regularly review and refactor your code. Look for repetitive patterns or bloated logic, and aim to simplify it. Tools like DRY (Don’t Repeat Yourself) can help you spot redundancies. Over time, small refactoring steps keep your code clean, efficient, and maintainable.

# If you see repeated code:
def print_welcome():
    print("Welcome!")

def print_goodbye():
    print("Goodbye!")

# Refactor into one function:
def print_message(message):
    print(message)

print_message("Welcome!")
print_message("Goodbye!")

Wrapping Up

Coding can feel like an art as much as a science, especially when balancing functionality with simplicity and efficiency. Avoiding these common mistakes will help us create cleaner, more reliable code, setting a strong foundation for more advanced development. It’s not about never making mistakes—it’s about learning from them and continuously improving. The more you code, the more these best practices become second nature. And remember, every mistake is just another step toward becoming a better coder!