Common coding mistakes and how to avoid them in interviews
Have you attended a coding interview or have one scheduled? We understand that coding interviews can be a high-pressure environment where even experienced developers can slip up, making it all the more overwhelming for beginners. You might be proficient in algorithms deeply, yet there’s always a subtle chance to falter, such as missing edge cases, resulting in runtime failures or rejected submissions.
To prevent unfortunate events, this guide covers some of the most common coding interview mistakes along with debugging strategies and a clean-code checklist to help you turn weaknesses into strengths.
Off-by-One errors
Off-by-one errors occur when your loop or array entries exceed boundaries, likely due to misusing “<” vs. “<=” or misunderstanding 0-based indexing.
Example – Incorrect loop range (Python)
# Wrong: Will crash on arr[len(arr)]
for i in range(len(arr) + 1):
print(arr[i])
# Correct
for i in range(len(arr)):
print(arr[i])
How to avoid them:
- Be cautious with start and end conditions in loops.
- Practice with small, edge-case arrays.
- Most loops use “<”; be careful when using “<” vs. “<=”.
Pro tip: Platforms like AlgoCademy highlight common boundary and indexing errors in real-time, offering interactive corrections.
Inefficient loops and nested operations
Many candidates often write nested loops with O(n²) complexity, when a more efficient O(n) or O(log n) solution is possible. This hurts both performance and perceived problem-solving skill.
Example – Unnecessary nested loops (Python)
# O(n²) – Bad
for i in range(len(arr)):
for j in range(len(arr)):
if arr[i] == arr[j] and i != j:
return True
# O(n) – Good (uses a set)
seen = set()
for num in arr:
if num in seen:
return True
seen.add(num)
How to avoid them:
- Use hash maps or sets to replace nested iteration.
- Rote time complexities to solve common operations.
- Already-processed elements do not need re-checking.
Neglecting edge cases
Ignoring edge-case testing is one of the most high-risk errors, even one missing case can fail your whole solution. A few of the common mistakes here can be failing to test inputs, like empty arrays, single elements, odd or even counts, maximum constraints, null values, negative numbers, etc..
Example (Python):
def find_max(arr):
if not arr: # Edge case check
return –1
max_val = arr[0]
for num in arr:
if num > max_val:
max_val = num
return max_val
How to avoid them:
- Ensure to write down test case categories before coding.
- Use TDD (Test-Driven Development): implement edge cases first.
- Add asserts or unit tests for inputs like [], [0], or [MAX_INT].
Poor variable naming and readability
Clear naming is one of the basics of coding. It should be understandable to both you and the interviewer. Writing ambiguous variable names can hide logic and invite errors.
Example – Bad vs. Good (Python):
# Unclear
x = 0
for i in lst:
x += i
# Clear
total_sum = 0
for num in numbers:
total_sum += num
Best practices:
- Always use descriptive names, like max_profit, left_pointer, counter.
- Avoid using single-character names unless inside a short loop.
- Follow language conventions, such as snake_case in Python, camelCase in JavaScript).
Incorrect recursion base cases
If you see stack overflows and infinite recursion, its likely that you forgot a base case, especially in tree or divide-and-conquer problems.
Example – No base case > Stack overflow (Python):
# Wrong: Infinite recursion
def factorial(n):
return n * factorial(n – 1)
# Correct
def factorial(n):
if n <= 1:
return 1
return n * factorial(n – 1)
How to avoid them:
- Always write the base case first.
- Use rote to cache repeated calls and prevent exponential time.
- Manually trace through small inputs.
Mutable default arguments
Mutable default arguments (like lists or dicts) retain state between function calls.
Example – The hidden bug (Python):
# Buggy: Default list persists across calls!
def add_item(item, lst=[]):
lst.append(item)
return lst
print(add_item(1)) # [1]
print(add_item(2)) # [1, 2] (Surprise!)
# Fixed
def add_item(item, lst=None):
if lst is None:
lst = []
lst.append(item)
return lst
How to fix them:
- Never use mutable default as parameters.
- Initialize them inside the function instead.
Skipping complexity optimization
Give importance to discussing or optimizing algorithmic complexity to display your understanding of the concept to the interviewer. If not, you might solve the problem correctly, but if it’s not optimal, interviewers will question your efficiency instincts.
Example (Python):
# Sorting first, then scanning — O(n log n)
nums.sort()
for i in range(1, len(nums)):
if nums[i] == nums[i–1]:
return True
# Better — O(n)
seen = set()
for num in nums:
if num in seen:
return True
seen.add(num)
After problem-solving, ask yourself:
- Can time or space be optimized?
- Are my data structures suitable?
- Can I reduce nested operations?
Debugging techniques & tools
| Technique | Purpose |
| IDE debugger | Step through, inspect variables, breakpoints |
| Linters/Static analysis | Catch code smells, unused code, and style issues |
| Test $ step through cases | Identify off-by-one, boundary logic errors |
| Profiling tools | Spot slow loops or heavy resource usage |
| Pair debugging (platform or peer) | Leverage external feedback and fresh eyes |
These techniques will help you spot subtle bugs early, give you systematic debugging, and build confidence under pressure.
Clean code checklist for interviews
Before you hit “Submit” or declare that you are done, implement the following:
- Understand & restate the problem
- Outline a plan before coding
- Start with brute force; then optimize
- Use precise naming and consistent formatting
- Handle edge cases explicitly
- Use efficient iteration: avoid manual index loops
- Check both time & space complexity
- Test with sample and boundary inputs
- Eliminate extraneous prints or warnings
- Reason your code and complexity through logic.
Wrap up
If you want to crack your coding interview with flying colors, only solving problems correctly won’t work; you should be capable to solve them cleanly, efficiently, and thoughtfully. Only then can you prevent these common coding errors from occuring, which are rather preventable with awareness and thorough preparation.
Platforms like AlgoCademy immensely help by offering real-time feedback, AI-powered error analysis, and step-by-step tutorials that accurately highlight subtle errors and guide you to correct them in context.
So, take your time, clear your thoughts, and aim to make it elegant and correct, not just writing to get it work.
