Why My LeetCode Solution Passes Examples But Fails Hidden Tests

You've spent 30 minutes on a LeetCode problem. Your solution passes every sample test case. You hit submit with confidence—and then see an explosion of red: Wrong Answer on test 47/58.

This is one of the most frustrating experiences in coding interview prep. Your code works, but it doesn't work enough.

The good news: there are predictable reasons why solutions fail hidden tests, and systematic ways to prevent it. Let's break down exactly why your LeetCode solution passes examples but fails hidden test cases—and how to fix it.

TL;DR

• Hidden tests are harder: They contain edge cases, large inputs, and unusual combinations that sample cases don't cover.
• Most failures fall into 4 categories: Edge cases, performance (TLE), integer overflow, and off-by-one errors.
• Sample cases are intentionally simple: They're designed to help you understand the problem, not validate your solution.
• Prevention beats debugging: Testing locally with edge cases before submission catches most issues.
• What you'll learn: A systematic checklist to validate solutions before hitting submit.

Why Sample Cases Are Deceptively Easy

They're Designed for Understanding, Not Validation

Sample test cases in LeetCode problems serve one purpose: help you understand what the problem is asking.

They're typically:

• Small and simple: Easy to trace through manually
• "Happy path": Normal cases without edge conditions
• Limited in variety: Usually just 2-3 examples

This makes them terrible for validating your solution. Passing them only means your code works for the simplest possible scenarios.

What Hidden Tests Actually Contain

Hidden tests include:

• Maximum input sizes: n = 10⁵ vs. the n = 5 in samples
• Edge cases: Empty arrays, single elements, all duplicates
• Boundary values: Maximum/minimum integers, zero, negative numbers
• Unusual combinations: Cases you wouldn't naturally think to test

Problem setters craft hidden tests specifically to catch incomplete solutions.

The Four Failure Categories

Almost every hidden test failure falls into one of these categories:

Category 1: Edge Cases (Wrong Answer)

Your logic works for normal inputs but fails on boundary conditions.

Common edge cases:

• Empty input: [] or ""
• Single element: [5] or "a"
• All same elements: [1,1,1,1]
• All zeros or negatives: [0,0,0] or [-5,-3,-1]
• Maximum/minimum values: [-2^31, 2^31-1]

Example failure:
You're finding the maximum subarray sum. Your code assumes at least one positive number exists—but the hidden test has [-5, -3, -1]. Expected answer: -1. Your answer: 0 or crash.

Category 2: Performance (TLE - Time Limit Exceeded)

Your algorithm is correct but too slow for large inputs.

Why samples don't catch this: Sample case has n = 10. Hidden test has n = 100,000. Your O(n²) solution works fine on 10 elements (100 operations) but times out on 100,000 elements (10 billion operations).

The fix: Analyze your algorithm's time complexity before submitting. See our guide on reading constraints to choose algorithms.

Category 3: Integer Overflow (Wrong Answer or Runtime Error)

Calculations exceed the range of your data type.

Common overflow scenarios:

• Summing large arrays: sum([10^9, 10^9, ...])
• Multiplying values: a * b where both are 10^9
• Intermediate calculations: Even if the final result fits, intermediate values might overflow

Example failure:

# This overflows in some languages
mid = (left + right) // 2  # If left = right = 2^30, sum overflows

# Safe version
mid = left + (right - left) // 2

Category 4: Off-by-One Errors (Wrong Answer)

Your loop or index is one position off from what it should be.

Common mistakes:

• Using < vs. <= in loops
• 0-indexed vs. 1-indexed confusion
• Exclusive vs. inclusive ranges
• Empty range handling in binary search

For a deep dive, see our article on debugging off-by-one errors in LeetCode array problems.

A Systematic Debugging Approach

When your solution fails hidden tests, follow this process:

Step 1: Read the Failed Test Case (If Shown)

LeetCode often shows the failing input. Look for:

• Input size (is it a performance issue?)
• Unusual values (edge case?)
• Patterns (all same, sorted, negative?)

Step 2: Reproduce Locally

Copy the failing test to your local environment. Add print statements to trace execution.

Step 3: Check Against the Failure Categories

Walk through each category:

1. Edge case: Did I handle empty/single/boundary inputs?
2. Performance: Is my algorithm fast enough for this input size?
3. Overflow: Are my calculations staying within type limits?
4. Off-by-one: Are my loop bounds and indices correct?

Step 4: Add the Failing Case to Your Test Suite

Once fixed, keep that test case. It represents a blind spot worth remembering.

Pre-Submission Checklist

Before clicking submit, run through this checklist:

Edge Case Verification

□ Empty input: [] or ""
□ Single element: [x]
□ Two elements (if relevant): [a, b]
□ All same values: [k, k, k, k]
□ Maximum constraint size: n = max_n
□ Minimum and maximum values: -10^9, 10^9
□ Zeros: [0, 0, 0]
□ Negative numbers (if allowed): [-5, -3, -1]

Complexity Check

□ Time complexity fits within constraints
□ Space complexity is acceptable
□ No unnecessary nested loops

Data Type Check

□ Used long/Long for sums of large values
□ No overflow in intermediate calculations
□ Binary search uses safe mid calculation

Boundary Check

□ Loop ranges are correct (< vs <=)
□ Array indices are within bounds
□ Handled empty ranges correctly

Code Example: Building a Test Harness

Here's a simple approach to test edge cases locally before submission:

def test_solution(solution_fn, test_cases):
    # Run solution against test cases and report results
    passed = 0
    failed = 0
    
    for i, (input_data, expected) in enumerate(test_cases):
        try:
            if isinstance(input_data, tuple):
                result = solution_fn(*input_data)
            else:
                result = solution_fn(input_data)
            
            if result == expected:
                passed += 1
                print(f"Test {i + 1} passed")
            else:
                failed += 1
                print(f"Test {i + 1} FAILED")
                print(f"  Input: {input_data}")
                print(f"  Expected: {expected}")
                print(f"  Got: {result}")
        except Exception as e:
            failed += 1
            print(f"Test {i + 1} ERROR: {e}")
    
    print(f"Results: {passed}/{passed + failed} passed")

# Example: Testing a two sum solution
def two_sum(nums, target):
    seen = {}
    for i, n in enumerate(nums):
        comp = target - n
        if comp in seen:
            return [seen[comp], i]
        seen[n] = i
    return []

# Standard + edge case tests
test_cases = [
    # Standard cases
    (([2, 7, 11, 15], 9), [0, 1]),
    (([3, 2, 4], 6), [1, 2]),
    
    # Edge cases
    (([3, 3], 6), [0, 1]),              # Duplicates
    (([-3, 4, 3, 90], 0), [0, 2]),      # Negative numbers
    (([1], 1), []),                      # Single element (no solution)
    (([0, 0], 0), [0, 1]),              # Zeros
]

test_solution(two_sum, test_cases)

Diagnosing Specific Error Types

Wrong Answer (WA)

What it means: Your output doesn't match expected output.

Debugging steps:

1. Check edge cases first
2. Trace through the failing input manually
3. Look for off-by-one errors
4. Check for overflow corrupting results
5. Verify you're returning the right thing (index vs. value, etc.)

Time Limit Exceeded (TLE)

What it means: Your code is too slow.

Debugging steps:

1. Calculate your time complexity
2. Check for nested loops that could be eliminated
3. Look for repeated work that could be cached
4. Consider a fundamentally different algorithm

Runtime Error (RE)

What it means: Your code crashed.

Common causes:

• Array index out of bounds
• Null/None pointer access
• Division by zero
• Stack overflow (infinite recursion)
• Integer overflow (in some languages)

Memory Limit Exceeded (MLE)

What it means: Your code uses too much memory.

Common causes:

• Allocating unnecessary large arrays
• Storing too many states in memoization
• Creating new objects in loops instead of reusing

Why Testing Your Own Code Matters

LeetCode's sample cases are a starting point, not validation. The difference between beginners who struggle with hidden tests and experienced problem-solvers who pass consistently is this: experienced solvers test their own edge cases before submission.

This habit takes 2-3 extra minutes per problem but saves 20+ minutes of debugging failed submissions.

Tools like LeetCopilot can help generate edge cases and validate your solution against unusual inputs before you submit. But even manually checking the edge case list above catches 80% of issues.

Building Edge Case Intuition

Over time, you'll develop intuition for which edge cases matter for different problem types:

Add these checks automatically when you see these problem types.

FAQ

Why doesn't LeetCode just show all test cases?
Hidden tests prevent memorization. They ensure you've solved the problem generally, not just for specific inputs.

How many edge cases should I test?
At minimum: empty, single element, and maximum size. Beyond that, depends on the problem. 5-10 tests total is usually sufficient.

Should I always test locally before submitting?
Ideally, yes. At minimum, mentally trace through edge cases before submission. Local testing is faster for complex problems.

What if I can't figure out why a hidden test fails?
If LeetCode doesn't show the input, try:

• Generating random large inputs
• Systematically testing edge cases
• Comparing against a known-correct brute force solution

How do I know if my solution is fast enough?
Use the constraint-to-complexity cheat sheet. If constraints say n <= 10^5, you need O(n log n) or better.

Conclusion

Hidden test failures aren't random—they follow predictable patterns: edge cases, performance limits, overflow, and off-by-one errors. By testing these categories before submission, you transform debugging from reactive frustration to proactive prevention.

Make edge case testing part of your problem-solving routine. The 2-3 minutes invested saves far more time than debugging failed submissions. More importantly, it builds the rigorous thinking that interviewers actually evaluate.

Your solution isn't done when it passes the samples. It's done when you've validated it against the edge cases hidden tests will check. Build this habit, and "Wrong Answer on test 47" becomes a rare occurrence instead of a regular frustration.