Originally published on LeetCopilot Blog

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Debugging is a critical skill for coding interviews, but most candidates struggle with it. Learn systematic debugging techniques, common mistake patterns, and practical strategies to find and fix bugs faster in your LeetCode solutions.

You’ve written your solution. The logic looks correct. You run the first test case—and it fails. You check your code again, but everything seems right. You add some print statements, run it again, and still get the wrong answer. Sound familiar?

Debugging LeetCode solutions is an art that many candidates underestimate. In real interviews, you’re expected to not just write code, but to identify and fix bugs quickly under pressure. The difference between candidates who pass and those who don’t often comes down to debugging skills, not just problem-solving ability.

This guide will teach you systematic debugging approaches, common mistake patterns to watch for, and practical techniques that will make you a more effective debugger. Whether you’re a beginner struggling with off-by-one errors or an experienced developer dealing with edge cases, these strategies will help you find bugs faster and write more robust code.

Why Debugging Skills Matter in Interviews

In coding interviews, debugging is often more important than writing perfect code on the first try. Interviewers want to see:

1. Problem-solving process: How you approach finding bugs
2. Systematic thinking: Whether you have a method, not just random guessing
3. Communication: Can you explain what you’re checking and why
4. Resilience: How you handle setbacks and incorrect solutions

Many candidates write code that’s 90% correct but fail because they can’t debug the remaining 10%. Learning to debug systematically will make you a stronger candidate.

The Systematic Debugging Framework

Here’s a step-by-step approach that works for most bugs:

Step 1: Reproduce the Bug Consistently

Before you can fix a bug, you need to understand when it occurs.

Actions:

• Identify the exact input that causes the failure
• Determine if it fails for all inputs or just specific cases
• Note the expected output vs. actual output

Example:

Input: [1, 2, 3, 4, 5]
Expected: 15
Actual: 10

Step 2: Isolate the Problem

Narrow down where the bug occurs.

Actions:

• Add print statements at key points
• Check intermediate values
• Verify assumptions about data state

Example:

def sum_array(arr):
total = 0
for i in range(len(arr)):
print(f"i={i}, arr[i]={arr[i]}, total={total}")  # Debug line
total += arr[i]
return total

Step 3: Form a Hypothesis

Based on your observations, form a theory about what’s wrong.

Common hypotheses:

• "I’m off by one in my loop bounds"
• "I’m not handling the empty case"
• "My variable isn’t being updated correctly"
• "I’m using the wrong comparison operator"

Step 4: Test Your Hypothesis

Make a targeted fix and test if it resolves the issue.

Important: Make one change at a time. If you change multiple things, you won’t know which fix worked.

Step 5: Verify the Fix

Test with the original failing case, plus edge cases.

Check:

• Does it work for the original failing input?
• Does it still work for previously passing cases?
• Does it handle edge cases (empty input, single element, etc.)?

Common Mistake Categories

Understanding common mistake patterns helps you know what to look for. Here are the most frequent categories:

Category 1: Off-by-One Errors

These are the most common bugs in array/string problems.

Mistake: Wrong Loop Bounds

Example Bug:

# Wrong: misses last element
for i in range(len(arr) - 1):
process(arr[i])

# Correct
for i in range(len(arr)):
process(arr[i])

How to Debug:

• Check if you’re processing all elements
• Verify start and end indices
• Test with arrays of different sizes (1, 2, 3 elements)

Mistake: Incorrect Index Calculations

Example Bug:

# Wrong: accessing out of bounds
for i in range(len(arr)):
if arr[i] == arr[i + 1]:  # i+1 can be out of bounds
# ...

# Correct
for i in range(len(arr) - 1):
if arr[i] == arr[i + 1]:
# ...

How to Debug:

• Always check if indices are within bounds before accessing
• Use len(arr) - 1 when comparing adjacent elements
• Add bounds checking: if i + 1 < len(arr)

Category 2: Initialization Errors

Forgetting to initialize variables or initializing them incorrectly.

Mistake: Uninitialized Variables

Example Bug:

def find_max(arr):
max_val  # Not initialized!
for num in arr:
if num > max_val:
max_val = num
return max_val

Correct:

def find_max(arr):
if not arr:
return None
max_val = arr[0]  # Initialize with first element
for num in arr:
if num > max_val:
max_val = num
return max_val

Mistake: Wrong Initial Value

Example Bug:

# Wrong: initializing max with 0 fails for negative numbers
def find_max(arr):
max_val = 0
for num in arr:
if num > max_val:
max_val = num
return max_val

# Correct
def find_max(arr):
if not arr:
return None
max_val = arr[0]  # or float('-inf')
for num in arr:
if num > max_val:
max_val = num
return max_val

How to Debug:

• Always initialize variables before use
• For max/min problems, initialize with the first element or ±infinity
• For counting problems, initialize with 0
• For boolean flags, initialize with False

Category 3: Edge Case Handling

Failing to handle boundary conditions.

Common Edge Cases to Check:

1. Empty input: empty array, empty string, None
2. Single element: [1], "a"
3. Two elements: [1, 2]
4. All same elements: [1, 1, 1, 1]
5. Extreme values: very large numbers, negative numbers
6. Special characters: in strings

Example Bug:

def reverse_string(s):
# Wrong: doesn't handle empty string
return s[::-1]

# Correct
def reverse_string(s):
if not s:
return s
return s[::-1]

How to Debug:

• Always test with empty input first
• Test with minimum valid input (1-2 elements)
• Test with maximum expected input size
• Consider what happens at boundaries

Category 4: Logic Errors in Conditionals

Wrong conditions in if statements or loops.

Mistake: Wrong Comparison Operator

Example Bug:

# Wrong: should be >= not >
if len(arr) > 0:  # Misses case when len is exactly 0
process(arr)

# Correct
if len(arr) >= 0:  # Or better: if arr:
process(arr)

Mistake: Incorrect Boolean Logic

Example Bug:

# Wrong: logic is inverted
if not (x > 0 and y > 0):
return True
else:
return False

# Correct
if x > 0 and y > 0:
return True
else:
return False

How to Debug:

• Test each branch of your conditionals
• Use truth tables for complex boolean logic
• Simplify conditions when possible
• Double-check De Morgan’s laws if using negations

Category 5: State Management Errors

Not updating state correctly, especially in loops or recursive functions.

Mistake: Forgetting to Update State

Example Bug:

def remove_duplicates(arr):
seen = set()
result = []
for num in arr:
if num not in seen:
result.append(num)
# Forgot to add to seen!
return result

Correct:

def remove_duplicates(arr):
seen = set()
result = []
for num in arr:
if num not in seen:
result.append(num)
seen.add(num)  # Update seen set
return result

Mistake: Updating State in Wrong Order

Example Bug:

# Wrong: updates before checking
def two_sum(arr, target):
seen = {}
for i, num in enumerate(arr):
seen[num] = i  # Updates before checking
if target - num in seen:
return [seen[target - num], i]

Correct:

def two_sum(arr, target):
seen = {}
for i, num in enumerate(arr):
if target - num in seen:
return [seen[target - num], i]
seen[num] = i  # Update after checking
return []

How to Debug:

• Trace through the algorithm step-by-step
• Verify state updates happen at the right time
• Check if state is being reset when it shouldn’t be
• Ensure all necessary state is being tracked

Category 6: Algorithm-Specific Mistakes

Bugs that are specific to certain algorithm patterns.

Sliding Window: Not Shrinking Correctly

Example Bug:

def longest_substring(s):
left = 0
char_set = set()
max_len = 0

for right in range(len(s)):
while s[right] in char_set:
char_set.remove(s[left])
left += 1
char_set.add(s[right])
max_len = max(max_len, right - left)  # Wrong: should be +1

return max_len

Correct:

def longest_substring(s):
left = 0
char_set = set()
max_len = 0

for right in range(len(s)):
while s[right] in char_set:
char_set.remove(s[left])
left += 1
char_set.add(s[right])
max_len = max(max_len, right - left + 1)  # +1 for inclusive

return max_len

Binary Search: Incorrect Mid Calculation or Bounds

Example Bug:

def binary_search(arr, target):
left, right = 0, len(arr) - 1

while left < right:  # Wrong: should be <=
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid  # Wrong: should be mid + 1
else:
right = mid  # Wrong: should be mid - 1

return -1

Correct:

def binary_search(arr, target):
left, right = 0, len(arr) - 1

while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1

return -1

Dynamic Programming: Wrong Recurrence Relation

Example Bug:

def climb_stairs(n):
dp = [0] * (n + 1)
dp[0] = 1
dp[1] = 1

for i in range(2, n + 1):
dp[i] = dp[i-1] + dp[i-1]  # Wrong: should be dp[i-2]

return dp[n]

Correct:

def climb_stairs(n):
dp = [0] * (n + 1)
dp[0] = 1
dp[1] = 1

for i in range(2, n + 1):
dp[i] = dp[i-1] + dp[i-2]  # Can come from i-1 or i-2

return dp[n]

Practical Debugging Techniques

Technique 1: Print Statement Debugging

The simplest but most effective technique.

What to Print:

• Variable values at key points
• Loop indices and values
• Function parameters and return values
• State of data structures (sets, maps, arrays)

Example:

def find_pair_sum(arr, target):
seen = {}
print(f"Target: {target}")  # Debug
for i, num in enumerate(arr):
print(f"i={i}, num={num}, seen={seen}")  # Debug
if target - num in seen:
return [seen[target - num], i]
seen[num] = i
return []

Best Practices:

• Use descriptive labels: print(f"After loop: i={i}, sum={sum}")
• Print before and after critical operations
• Remove or comment out debug prints before submission

Technique 2: Rubber Duck Debugging

Explain your code line-by-line to an inanimate object (or a person). Often, you’ll catch the bug while explaining.

Process:

1. Read your code out loud
2. Explain what each line does
3. Explain what you expect to happen
4. Compare expectations with actual behavior

Technique 3: Test Case Reduction

Simplify the failing test case to find the minimal input that reproduces the bug.

Process:

1. Start with the failing input
2. Remove elements one at a time
3. Test if the bug still occurs
4. Find the smallest input that fails

Example:

• Original failing input: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
• Try: [1, 2, 3] — still fails
• Try: [1, 2] — still fails
• Try: [1] — works
• Minimal failing case: [1, 2] — easier to debug!

Technique 4: Code Tracing

Manually trace through your code with a specific input.

Process:

1. Pick a small test case
2. Write down variable values after each step
3. Compare with what you expect
4. Find where values diverge

Example Trace:

Input: [2, 7, 11, 15], target = 9

Step 1: i=0, num=2, seen={}, target-num=7 not in seen
seen={2: 0}

Step 2: i=1, num=7, seen={2: 0}, target-num=2 in seen!
Return [0, 1] ✓

Technique 5: Assertion-Based Debugging

Add assertions to verify your assumptions.

Example:

def binary_search(arr, target):
left, right = 0, len(arr) - 1

assert left <= right, f"Invalid bounds: left={left}, right={right}"

while left <= right:
mid = (left + right) // 2
assert 0 <= mid < len(arr), f"Mid out of bounds: mid={mid}"

if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1

return -1

Technique 6: Compare with Known Good Solution

If you have a reference implementation, compare step-by-step.

Process:

1. Run both solutions with the same input
2. Compare intermediate values
3. Find the first point where they diverge
4. That’s where your bug is

Debugging Workflow for Interviews

In an interview setting, follow this structured approach:

1. Acknowledge the Bug

Don’t panic. Say: "I see the output doesn’t match. Let me trace through this."

2. Reproduce with a Simple Case

Start with the smallest possible input that fails.

3. Trace Through Manually

Walk through your code step-by-step, explaining what should happen.

4. Identify the Issue

Point out where the logic diverges from expectations.

5. Fix and Verify

Make the fix, then trace through again to confirm.

6. Test Edge Cases

Verify the fix works for edge cases too.

Example Interview Dialogue:

Interviewer: "Your solution returns 10, but we expected 15."

You: "Let me trace through with the input [1, 2, 3, 4, 5].
I initialize sum to 0. Then I loop from i=0 to i=4...
Wait, I see the issue—I'm using range(len(arr) - 1)
which stops at index 3, missing the last element.
Let me fix that to range(len(arr))."

Common Patterns to Watch For

Pattern 1: Modifying While Iterating

Bug:

arr = [1, 2, 3, 4, 5]
for i in range(len(arr)):
if arr[i] % 2 == 0:
arr.remove(arr[i])  # Modifying while iterating!

Fix:

arr = [1, 2, 3, 4, 5]
arr = [x for x in arr if x % 2 != 0]  # List comprehension
# Or iterate backwards

Pattern 2: Shallow vs Deep Copy

Bug:

matrix = [[0] * 3] * 3  # Creates references, not copies!
matrix[0][0] = 1  # Changes all rows!

Fix:

matrix = [[0] * 3 for _ in range(3)]  # Creates separate lists

Pattern 3: Integer Division vs Float Division

Bug:

mid = (left + right) / 2  # Float division, might cause issues

Fix:

mid = (left + right) // 2  # Integer division

Tools and Resources for Debugging

Built-in Debugging Tools

Most IDEs and online judges provide:

• Step-through debugging: Execute line by line
• Variable inspection: See values at each step
• Breakpoints: Pause execution at specific lines
• Call stack: See function call hierarchy

LeetCode-Specific Tips

1. Use the "Run" button frequently — Don’t wait until the end
2. Test with custom input — Try edge cases yourself
3. Check the "Expected vs Output" — It shows exactly what differs
4. Use the discussion section — But only after you’ve tried debugging yourself

Integration with Learning Tools

When debugging becomes challenging, tools that provide step-by-step hinting system can help you understand where your logic might be going wrong without immediately revealing the solution. The key is getting guidance on what to check rather than seeing the fix directly.

For systematic practice, following a structured DSA learning path helps you encounter common bugs in a controlled way, building debugging intuition alongside problem-solving skills.

FAQ

Q: How long should I spend debugging before looking at solutions?

A: For learning: 15-30 minutes of focused debugging is valuable. If you’re completely stuck after that, it’s okay to look at solutions, but make sure you understand why your approach was wrong.

Q: Should I debug on paper or in the IDE?

A: Both have value. Paper helps with understanding logic flow. IDE helps with actual execution. Start with paper for logic errors, use IDE for runtime errors.

Q: How do I debug recursive functions?

A: Add print statements at the start of each recursive call showing the parameters. Track the call stack depth. Verify base cases are reached. Check that recursive calls are moving toward the base case.

Q: What if my code works for some test cases but not others?

A: This usually indicates an edge case bug. Identify what’s different about the failing cases. Common differences: size (empty, single element), values (negative, zero, very large), or structure (sorted vs unsorted).

Q: How can I prevent bugs in the first place?

A: Write code incrementally and test frequently. Handle edge cases early. Use meaningful variable names. Add comments for complex logic. Think through the algorithm before coding.

Q: Should I use a debugger or print statements?

A: Print statements are faster for quick checks and work everywhere. Debuggers are better for complex control flow. In interviews, you’ll likely use print statements, so practice with those.

Q: How do I debug time limit exceeded errors?

A: Check for infinite loops (while conditions that never become false). Look for redundant calculations that could be memoized. Verify your algorithm’s time complexity matches the problem constraints. Consider if there’s a more efficient approach.

Conclusion

Debugging is a skill that improves with practice. The systematic approaches outlined here—reproducing bugs, isolating problems, forming hypotheses, and testing fixes—will serve you well in both practice and interviews.

Remember these key principles:

• Be systematic: Don’t guess randomly; follow a method
• Start simple: Use the smallest failing case
• Trace carefully: Understand what your code actually does, not what you think it does
• Learn patterns: Common bugs follow patterns; recognizing them speeds up debugging
• Practice regularly: The more you debug, the faster you’ll become

The ability to quickly identify and fix bugs is often what separates candidates who pass interviews from those who don’t. With consistent practice using these techniques, debugging will become a strength rather than a weakness.

Most importantly, don’t get discouraged when you encounter bugs. Every bug you fix makes you a better programmer. Each debugging session teaches you something new about how code actually behaves versus how you expect it to behave. Embrace the process, and you’ll find that debugging becomes one of the most satisfying parts of programming.

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