Why My LeetCode Solution Passes Some Test Cases But Fails Others

Your solution passes 15 out of 20 test cases. The sample inputs work perfectly. But somewhere in the hidden tests, something breaks—and LeetCode won't tell you exactly what.

This is one of the most frustrating experiences in coding interview prep. You almost have it, but not quite. And without seeing the failing input, finding the bug feels like searching for a needle in a haystack.

The good news: partial failures follow predictable patterns. Most are caused by a handful of common issues—edge cases, off-by-one errors, constraint violations, or subtle logical flaws. This guide teaches you how to systematically identify what's breaking when your solution is "almost right."

TL;DR

• Partial pass usually means edge cases or boundary conditions. Your core logic is likely correct, but fails on specific inputs.
• The most common culprits: empty inputs, single elements, maximum/minimum values, duplicates, and off-by-one indexing errors.
• Systematic debugging trumps guessing. Generate test cases methodically instead of randomly hoping to find the bug.
• Look at which test cases pass vs. fail. Early passes with late failures often indicate constraint-related issues or extreme values.
• The bug is usually simpler than you think. Most partial failures come from overlooked edge cases, not fundamental algorithm flaws.

Beginner-Friendly Explanations

Why Partial Failures Happen

When code passes some tests but fails others, it typically means:

1. Your algorithm is mostly correct — You understood the problem and implemented a valid approach
2. Your implementation misses specific cases — Certain inputs expose gaps in your logic

This is actually better than getting zero passes. A complete failure suggests a fundamental misunderstanding; partial failure suggests you're close but missing something.

The Hidden Test Case Challenge

LeetCode's hidden test cases are designed to catch:

• Edge cases: Empty arrays, single elements, maximum size inputs
• Boundary values: Smallest/largest numbers allowed by constraints
• Special patterns: All duplicates, all same value, sorted/reverse-sorted
• Performance limits: Inputs at the maximum constraint size

Understanding what LeetCode tests for helps you anticipate what might break.

Step-by-Step Learning Guidance

Step 1: Analyze the Failure Pattern

Before debugging, gather information:

1. How many tests passed vs. failed? (e.g., "47/53 passed")
2. What type of failure? (Wrong Answer, Time Limit Exceeded, Runtime Error)
3. Does LeetCode show the failing input? (Sometimes it does for Wrong Answer)

Pattern interpretation:

Step 2: Generate Edge Case Test Inputs

Systematically create inputs that stress your solution. Here's a checklist:

For Array Problems:

[ ]                    // Empty array
[1]                    // Single element
[1, 2]                 // Minimum interesting size
[1, 1, 1, 1]           // All duplicates
[1, 2, 3, ..., n]      // Sorted ascending
[n, n-1, ..., 1]       // Sorted descending
[max_val, min_val]     // Constraint boundaries

For String Problems:

""                     // Empty string
"a"                    // Single character
"aaaa"                 // All same character
"constraint_max_len"   // Maximum length string

For Number Problems:

0                      // Zero
1                      // One
-1                     // Negative one
MAX_INT                // Maximum integer value
MIN_INT                // Minimum integer value

Test each of these against your solution before submitting.

Step 3: Look for Off-by-One Errors

Off-by-one errors are the most common cause of partial failures. Check every loop and index access:

// Common off-by-one patterns to audit

// Loop boundaries
for (let i = 0; i < arr.length; i++)     // Correct for full array
for (let i = 0; i <= arr.length; i++)    // ERROR: accesses arr[length]
for (let i = 1; i < arr.length; i++)     // Misses first element
for (let i = 0; i < arr.length - 1; i++) // Misses last element

// Array access
arr[i - 1]  // Fails when i = 0
arr[i + 1]  // Fails when i = length - 1

// Substring/slice
str.slice(0, n)    // Characters 0 to n-1 (n not included)
str.substring(0, n) // Same as slice

Step 4: Check Constraint Boundaries

Read the problem constraints carefully. Then verify your code handles:

Size constraints:

• Does your code handle n = 0?
• Does your code handle n = 1?
• Does your code handle n = max_constraint?

Value constraints:

• If values can be negative, does your code handle that?
• If values can be 0, does your code handle that?
• Do you risk integer overflow with large values?

Example: If the constraint says "1 ≤ n ≤ 10^5" and values can be up to 10^9, then sum = values[i] + values[j] could overflow a 32-bit integer.

Step 5: Trace Through a Failing Input

When you find (or suspect) a failing input, manually trace your code:

1. Write down initial variable values
2. Step through each line, updating values
3. Compare your trace to expected behavior
4. Identify where the first deviation occurs

This relates to the manual code tracing technique essential for debugging.

Step 6: Add Targeted Print Statements

If tracing manually doesn't reveal the bug, add print statements at critical points:

function solve(nums: number[]): number {
    console.log("Input:", nums);
    
    for (let i = 0; i < nums.length; i++) {
        // ... logic
        console.log(`After i=${i}: state=`, state);
    }
    
    console.log("Final result:", result);
    return result;
}

Run with your edge cases and compare the trace to your expectations.

Common Causes and Fixes

Cause 1: Empty Input Not Handled

Symptom: Passes all tests except those with empty arrays or strings.

Example bug:

function findMax(nums: number[]): number {
    let max = nums[0];  // ERROR: crashes if nums is empty
    // ...
}

Fix:

function findMax(nums: number[]): number {
    if (nums.length === 0) return -Infinity; // or appropriate default
    let max = nums[0];
    // ...
}

Cause 2: Single Element Edge Case

Symptom: Works for arrays of length 2+, fails for length 1.

Example bug:

function twoSum(nums: number[], target: number): number[] {
    for (let i = 0; i < nums.length; i++) {
        for (let j = i + 1; j < nums.length; j++) {
            // With length 1, inner loop never executes
        }
    }
    return [];  // Returns empty for single-element input
}

This may or may not be a bug depending on problem requirements—check if single-element input is valid.

Cause 3: Off-by-One in Loop Bounds

Symptom: Missing first or last element in results.

Example bug:

// Finding max in sliding window
for (let i = 0; i < nums.length - k; i++) {  // Should be <= 
    // Process window starting at i
}
// Misses the last valid window

For more on sliding window debugging, see our sliding window debug checklist.

Cause 4: Integer Overflow

Symptom: Works on small inputs, wrong answer on large values.

Example:
Problem: "Find the sum of all elements"
Constraints: n up to 10^5, values up to 10^9

// Bug: sum can exceed 32-bit integer
let sum = 0;
for (const num of nums) {
    sum += num;  // 10^5 * 10^9 = 10^14, overflows 32-bit int
}

Fix: In JavaScript, numbers are 64-bit floats, so this specific case is okay. In Java/C++, use long instead of int.

Cause 5: Incorrect Comparison Operators

Symptom: Off by one in results, or boundary values handled wrong.

Example bug:

// Should include equal values but uses strict inequality
if (current > target) {  // Should be >=
    // ...
}

Audit every <, >, <=, >=, ==, === in your code.

Cause 6: Not Resetting State Between Iterations

Symptom: First few test cases pass, later ones fail mysteriously.

Example bug (in LeetCode's class-based problems):

class Solution {
    private cache = new Map();  // Not cleared between test cases!
    
    solve(input: number[]): number {
        // Uses stale cache from previous test
    }
}

Fix: Initialize state inside the method, not as class properties.

Practical Preparation Strategies

Strategy 1: The Pre-Submit Checklist

Before hitting Submit, test these manually:

1. ☐ Empty input (if allowed by constraints)
2. ☐ Single element input
3. ☐ Maximum constraint size (conceptually verify complexity)
4. ☐ Boundary values (0, 1, -1, max, min)
5. ☐ All same values
6. ☐ The given examples (sanity check)

Strategy 2: Think Like a Test Writer

Ask yourself: "If I were writing test cases to break solutions, what would I test?"

• What's the smallest valid input?
• What's the largest?
• What patterns might trip up common approaches?
• What values are "special" (zero, negative, duplicates)?

Strategy 3: Binary Search Your Bug

If you have many test cases failing, find the simplest failing input:

1. Start with the smallest possible input
2. Gradually add complexity until it fails
3. The transition point reveals your bug

This minimizes the input you need to debug.

Strategy 4: Use Guided Debugging Tools

When stuck on a tricky edge case, tools like LeetCopilot can help you think through what inputs might cause failure without giving away the full solution. This structured guidance builds debugging intuition over time.

Example: Complete Debugging Walkthrough

Problem: "Find the maximum subarray sum"

Your solution:

function maxSubArray(nums: number[]): number {
    let maxSum = 0;
    let currentSum = 0;
    
    for (const num of nums) {
        currentSum = Math.max(num, currentSum + num);
        maxSum = Math.max(maxSum, currentSum);
    }
    
    return maxSum;
}

Symptom: Passes 200/203 tests. Wrong answer on some.

Debugging process:

1. Test edge cases:

   • [1, 2, 3] → returns 6 ✓
   • [-1] → returns 0 ✗ (expected -1)
   • [-2, -1] → returns 0 ✗ (expected -1)

2. Identify the bug: When all numbers are negative, the answer should be the largest negative number, but maxSum = 0 as initial value skips all negatives.

3. Fix:

function maxSubArray(nums: number[]): number {
    let maxSum = nums[0];  // Start with first element, not 0
    let currentSum = nums[0];
    
    for (let i = 1; i < nums.length; i++) {
        currentSum = Math.max(nums[i], currentSum + nums[i]);
        maxSum = Math.max(maxSum, currentSum);
    }
    
    return maxSum;
}

Root cause: Initializing maxSum to 0 assumed at least one non-negative number exists.

FAQ

How do I find the failing test case when LeetCode doesn't show it?
Generate edge cases systematically using the checklist above. Most hidden test failures are predictable edge cases, not random inputs.

Should I always fix bugs before understanding them?
No. Understand why it failed first, then fix. Otherwise, you might introduce new bugs or create fixes that only work by coincidence.

What if my solution times out on later test cases but logic is correct?
That's a complexity issue, not a correctness issue. Your algorithm works but is too slow. You need a more efficient approach, not bug fixes.

How do I know if it's an edge case bug vs. a fundamental algorithm flaw?
Passing 80%+ of tests usually indicates edge case issues. Passing under 50% suggests algorithm problems. If the sample cases work but hidden ones fail, it's almost always edge cases.

My code works in my local environment but fails on LeetCode. Why?
Common causes: different language versions, global variables not reset between tests, or relying on uninitialized default values that differ between environments.

Conclusion

Partial test failures are frustrating but predictable. Most come from a small set of causes: edge cases (empty, single element), boundary values (max/min), off-by-one errors, and initialization bugs.

The key is systematic debugging rather than random guessing. Generate edge cases before submitting, check your loop bounds and comparison operators, and trace through failing inputs manually.

When you develop the habit of pre-testing edge cases and auditing common bug locations, partial failures become rare. And when they do happen, you'll know exactly where to look.

The next time your solution passes "most" tests, don't despair—treat it as useful information. Those failing tests are telling you exactly what your code doesn't handle. Listen to them, fix the gaps, and watch your acceptance rate climb.