Writing Structured Code - Cracking the Coding Interview

In coding interviews, writing clean, efficient, and structured code is crucial. Not only does it demonstrate your technical ability, but it also shows that you can approach problems in a logical, organized manner. Structured code is easier to understand, maintain, and debug—qualities that are highly valued in a software engineer.

Writing structured code is not just about solving a problem—it’s about solving it in a way that is efficient, readable, and maintainable. In this article, we will explore what it means to write structured code in the context of coding interviews, provide tips and tricks to help you do so, and show how this can impact your performance.

1. What Is Structured Code?

Structured code refers to code that follows principles that enhance clarity, readability, and maintainability. It involves organizing your code in a way that is logical and straightforward, making it easy for someone else (or even yourself) to understand the logic at any time.

Key Characteristics of Structured Code:

• Clarity: Your code should clearly express its intent. Anyone reading your code should be able to easily follow your thought process.
• Modularity: Break your code into smaller, reusable functions or methods that each perform a specific task.
• Consistency: Use consistent naming conventions, indentation, and formatting throughout your code.
• Efficiency: Your code should be optimized for time and space complexity, particularly when dealing with large inputs.
• Readability: Ensure that your code is easy to read, understand, and debug. Avoid overly complex solutions or unnecessary convolutions.

2. Why Is Structured Code Important in Interviews?

In coding interviews, interviewers assess not only your ability to solve problems but also how well you structure your code. Writing structured code is important for several reasons:

• Demonstrates Problem Solving Skills: Structured code reflects how well you’ve thought through a problem. An interviewer can tell that you’ve considered all aspects of the problem when your code is modular and clean.
• Increases Readability: If the interviewer needs to review your code or debug it, well-structured code makes it easier to understand what you’ve done and why.
• Improves Maintainability: Structured code can be easily modified or extended, making it easier to handle changes or improvements down the line.
• Reflects Professionalism: Writing clean and organized code demonstrates that you adhere to industry standards. It shows that you are conscious of the real-world implications of your work, where code readability and maintainability are essential.

3. Tips for Writing Structured Code in Interviews

Here are some tips that will help you write structured code during your coding interviews:

1. Plan Before You Code

The first step in writing structured code is planning. Take some time to break down the problem and come up with a strategy before jumping into writing code. Follow these steps:

• Understand the problem: Make sure you clearly understand the requirements, inputs, and expected outputs of the problem.
• Identify edge cases: Consider potential edge cases, such as empty inputs, large inputs, or boundary values.
• Choose the right algorithm: Think about which algorithm or data structure is best suited for the task (e.g., sorting, searching, recursion).
• Break the problem into smaller parts: Decompose the problem into smaller subproblems that are easier to manage. This will help you design modular functions.

2. Use Meaningful Variable and Function Names

Variable and function names should clearly convey the purpose of the variable or function. This improves readability and makes your code easier to understand at a glance.

• Bad naming: x, temp, a1, arr1
• Good naming: maxSum, findDuplicate, numbersList, studentGrades

Your code should be self-explanatory to someone who reads it for the first time.

3. Break Code into Functions

Writing functions that each handle one specific task makes your code easier to follow and test. Functions should be modular and concise, performing one job efficiently.

• Bad function: A function that handles both input validation and sorting.
• Good function: A function that just validates input and another that sorts the data.

Here’s an example of a modular approach:

# Bad example: single function handling multiple tasks
def process_data(data):
    if not data:
        return []
    data.sort()
    return data

# Good example: separate concerns into distinct functions
def validate_data(data):
    return data if data else []

def sort_data(data):
    return sorted(data)

def process_data(data):
    validated_data = validate_data(data)
    sorted_data = sort_data(validated_data)
    return sorted_data

Each function has a single responsibility, making the code easier to read and test.

4. Write Clear and Concise Comments

While your code should be self-explanatory, comments are essential when you need to explain non-obvious logic. Good comments clarify the purpose of code or the reasoning behind a complex solution.

• Bad comment: # This does something
• Good comment: # Check if the number is prime by testing divisibility from 2 to sqrt(num)

However, don't over-comment. Comments should be used to explain "why" something is done, not "what" is done (since the code should already answer that).

5. Consistent Formatting and Indentation

Maintain consistent indentation and spacing throughout your code. This improves the readability and visual organization of your code.

• Bad formatting:

  if x>10:
  print("Too large")
  

• Good formatting:

  if x > 10:
      print("Too large")
  

Consistent indentation is crucial, as it helps to visually separate blocks of code, making it easier to understand.

6. Use Built-in Functions and Libraries Wisely

Whenever possible, use the built-in functions and libraries provided by the language you're using. These functions are often optimized for performance and correctness. For example, in Python:

• Use sorted() instead of writing your own sorting algorithm.
• Use Counter from the collections module to count occurrences in a list.
• Use map() or list comprehensions for cleaner iteration over data.

By leveraging built-in methods, you can focus on the core logic of the problem rather than reimplementing common functionality.

7. Avoid Hardcoding Values

Hardcoding values (such as magic numbers or arbitrary constants) makes your code less flexible and harder to maintain. Instead, use constants, configuration files, or parameters that can be easily adjusted.

• Bad practice:

  for i in range(1, 10):
      print(i)
  

• Good practice:

  START = 1
  END = 10
  for i in range(START, END):
      print(i)
  

By using named constants, your code becomes easier to change if the value needs to be adjusted.

8. Optimize Code for Time and Space Complexity

While writing structured code is essential, efficiency is also key in coding interviews. Always aim for the optimal solution in terms of time and space complexity.

• Time complexity: Focus on reducing the number of operations your code performs. Avoid O(n^2) solutions unless absolutely necessary.
• Space complexity: Be mindful of the memory your code consumes. In some cases, you might need to optimize space usage to avoid memory limitations.

For example, if you're solving a problem where you need to find duplicates in an array, using a hash set can reduce time complexity from O(n^2) (brute force) to O(n) (efficient).

4. Example Problem: Writing Structured Code for Two-Sum

Let’s apply structured coding principles to the Two-Sum problem, which is a common coding interview question.

Problem:

Given an array of integers and a target sum, find two numbers in the array that add up to the target sum. Return their indices.

Structured Code:

def two_sum(nums, target):
    #  Step 1: Initialize an empty dictionary to store the numbers we've seen.
    seen = {}

    #  Step 2: Loop through the array
    for i, num in enumerate(nums):
        #  Step 3: Calculate the complement
        complement = target - num
        
        #  Step 4: Check if the complement is already in the dictionary
        if complement in seen:
            #  If found, return the indices of the two numbers
            return [seen[complement], i]
        
        #  Step 5: Add the current number and its index to the dictionary
        seen[num] = i
    
    #  Return an empty list if no solution is found
    return []

Explanation:

• We initialize a dictionary (seen) to keep track of the numbers we’ve already encountered.
• As we iterate through the array, we calculate the complement (i.e., target - num).
• If the complement is in the dictionary, we’ve found a pair of numbers that sum to the target and return their indices.
• This solution is efficient, with a time complexity of O(n).

Key Structured Coding Principles:

1. Modular Design: The function is focused solely on solving the Two-Sum problem, and no extraneous logic is included.
2. Clear Variable Names: Variable names like seen, complement, and num are descriptive and easy to understand.
3. Efficient Code: By using a dictionary to store seen numbers, we achieve O(n) time complexity instead of a brute force O(n^2) solution.

5. Conclusion

Writing structured code is essential in coding interviews, as it demonstrates both your technical abilities and your approach to solving problems. By following key principles—planning, modularity, clear variable names, and consistency—you can write code that is efficient, readable, and easy to maintain. Structured code also reflects a professional attitude, showing that you are thinking about how others will interact with your code and how it will scale in real-world applications.

Key Takeaways:

• Plan before coding to break down the problem and identify edge cases.
• Use meaningful names for variables and functions to make the code self-explanatory.
• Write modular code by breaking down the problem into smaller, reusable functions.
• Follow best practices for formatting, commenting, and efficiency.
• Optimize for time and space complexity to solve problems efficiently.

By adopting these practices, you’ll not only excel in coding interviews but also be prepared for real-world software development challenges. Structured code will help you write better, more maintainable solutions that stand the test of time.