AI Debugging for LeetCode: Step-by-Step Ways Beginners Can Fix Runtime Errors Fast

Why Runtime Errors Feel Harder Than Wrong Answers

For new grads and self-taught engineers, a failing test with no clear clue can feel worse than a wrong output. The fix is adopting a debug-first mental model: reproduce, instrument, isolate. When you add lightweight tracing and consistent inputs, even tricky segmentation faults and timeouts become predictable.

What Counts as a “Runtime Error” (and Why It Happens)

Crash vs. hang vs. silent failure

• Crash: Null pointer, index out of bounds, divide by zero, or segfault. Usually deterministic and fast to reproduce.
• Hang/timeout: Infinite loops, unbounded recursion, or an algorithm that is secretly O(N^3). Needs a time-budget lens.
• Silent wrongness: Logic error that passes samples but fails hidden tests. Requires intentional edge-case design.

Root causes beginners miss

• Unvalidated assumptions: “array is sorted,” “input is non-empty,” “graph is connected.”
• Mutation side-effects: Reusing global state between tests or mutating input inadvertently.
• Off-by-one on boundaries: while left < right vs. <=; slicing errors; fencepost issues in loops.
• Numeric overflow: Summing into int when long is needed (Java/C++).
• State leakage in recursion: Forgetting to undo a push/pop, visited flag, or memo table entry.
• Floating-point drift: Comparing doubles directly instead of with tolerance.

A Simple, Repeatable Debugging Flow

Step 1: Reproduce with the smallest input

• Reduce the failing case until it breaks with 3–6 elements or characters.
• Clarify constraints (array size, negative values, duplicates) to avoid overfitting.

Step 2: Add structured logging

Prefer deterministic, low-noise logs:

def two_sum(nums, target):
    seen = {}
    for i, n in enumerate(nums):
        print(f"i={i}, n={n}, seen={seen}")
        if target - n in seen:
            return [seen[target - n], i]
        seen[n] = i
    return []

Explain logs as you go: “At i=3, we saw 4 earlier, target is 9, so 9-4=5 was missing.” This is how you narrate in interviews, too.

Step 3: Isolate the failing branch

Add guards that fail fast instead of silently:

• Check index bounds before access.
• Validate input assumptions (sorted? non-empty? unique?).
• Add timeouts around expensive loops when practicing locally.

Step 4: Generate edge cases on purpose

Cover “holes” systematically:

• Empty, single element, two elements.
• Duplicates and negative values.
• Max constraints (length, value range).

Step 5: Compare against a slow-but-correct baseline

Write a brute-force or backtracking version for tiny inputs and compare outputs:

def brute_force(nums):
    # Example: brute subset sum check for tiny arrays
    from itertools import combinations
    best = -1
    for r in range(len(nums) + 1):
        for comb in combinations(nums, r):
            best = max(best, sum(comb))
    return best

If optimized and brute disagree on length ≤ 8, you have a logic bug, not a performance issue.

Step 6: Stabilize with guards and invariants

• Assertions (assert left <= right, assert len(stack) >= 0) help you fail fast and locate the line.
• Track invariants in comments: “window always holds unique chars,” “heap stores k largest so far,” “visited prevents revisits.”

Step 7: Write a mini post-mortem

Two lines: Root cause (e.g., window shrink order) and Guardrail (e.g., add assert or helper that enforces order). Drop it into your personal coding interview guide so you can revisit before interviews.

Using AI Without Losing the Learning

Tools like LeetCopilot can surface a step-by-step hinting system that keeps you in control while revealing just enough state to move forward. Ask for a trace of your current code rather than a final answer—this preserves your reasoning skill and still accelerates debugging. You can also kick off an AI-guided LeetCode practice session that auto-generates 3–5 edge cases tailored to your code, so you stress-test without leaving the editor.

Example: Debugging an Off-By-One in Sliding Window

Suppose you’re counting subarrays with sum ≥ k. A common bug is shrinking too late:

def min_subarray_len(k, nums):
    total = 0
    left = 0
    res = float("inf")
    for right, val in enumerate(nums):
        total += val
        while total >= k:
            res = min(res, right - left + 1)
            total -= nums[left]
            left += 1
    return 0 if res == float("inf") else res

Trace it on [2,3,1,2,4,3], k=7 and log left, right, total, res. If res never updates, you know the while never triggers—pointing to a constraint misunderstanding, not the loop itself. If it times out, log how many times the while fires; if it fires excessively, you’re probably not advancing left correctly.

Binary Search Debug Pattern

For binary search bugs, log mid, condition, and boundaries every loop:

function lowerBound(arr: number[], target: number): number {
  let lo = 0, hi = arr.length;
  while (lo < hi) {
    const mid = Math.floor((lo + hi) / 2);
    console.log({ lo, hi, mid, val: arr[mid] });
    if (arr[mid] < target) lo = mid + 1;
    else hi = mid;
  }
  return lo;
}

If lo stops moving, your condition is wrong; if hi underflows, your mid math is off. Pair this with a brute-force linear scan on tiny arrays to validate behavior.

DFS/BFS Debug Pattern

• Print entering and exiting a node to catch missing backtracking steps.
• Log queue length each iteration to see if you’re stuck in cycles.
• For grid problems, print coordinates and mark visited visually (small matrix dump) to spot leaks.

Interview-Focused Debug Habits

Narrate intent, not just actions

Say, “I’m logging left/right to confirm the window shrinks after reaching k,” instead of “I added a print.”

Keep a reusable checklist

• Validate input size and ordering.
• Print loop counters at entry/exit.
• Check for integer overflow or type mismatches.

Show observability thinking

Mention how you’d keep these logs as structured metrics in production, even if you strip them out for LeetCode.

Build a reusable “debug harness”

Create a quick harness you can paste into LeetCode’s console:

def debug_case(fn, inputs, expected=None):
    print("Running", inputs)
    got = fn(*inputs)
    print("Result", got)
    if expected is not None:
        print("Expected", expected, "PASS" if got == expected else "FAIL")

Use it to run 3–5 seeds before submitting, especially when dealing with randomness or hash collisions.

Make the hidden test visible (mentally)

• Ask: “What constraint could flip this?” Try max size, all equal, strictly increasing, strictly decreasing, zeros/negatives.
• State your invariant out loud; if you can’t, you probably don’t have one, and the bug hides there.

Common Mistakes to Avoid

• Guessing new algorithms before reproducing the bug.
• Logging too much (noise hides the signal).
• Ignoring constraints; a fix that fails on max input is still a fail.
• Refusing to write a brute-force comparator—helpful for binary search or greedy bugs.
• Treating timeouts as “just optimize later” instead of proving correctness on small inputs first.
• Copying discussion solutions without understanding the invariants you violated.

Where AI Fits in the Debug Loop

Leverage AI to generate targeted edge cases or a quick execution trace, then validate with your own reasoning. A light touch—like pairing with a mock interview simulator after you fix the bug—helps you practice explaining the root cause under time pressure. If you maintain a personal coding interview guide, link your toughest debugging cases there so you can revisit patterns (null checks, overflow, window shrink timing) before real interviews.

Language-Specific Debug Tips

Python

• Watch for mutable default arguments (def fn(x, arr=[])).
• Use enumerate instead of manual indexes to avoid off-by-one.
• Prefer bisect for correctness in binary search tasks; log slices sparingly.

Java

• Guard against NullPointerException by validating inputs; prefer List.get(i) bounds checks early.
• Use long for sums/products, especially in prefix sums and sliding windows.
• Leverage Arrays.toString for quick state dumps; remove before final submit if needed.

C++

• Initialize vectors to correct sizes; uninitialized reads cause flaky behavior.
• Use size_t carefully; mixing signed/unsigned can make loops never terminate.
• Prefer std::array or fixed-size buffers when constraints are known; add assert(i < n) in debug mode.

JavaScript/TypeScript

• Distinguish between == and ===; strict equality prevents surprise coercion.
• Copy arrays/objects when needed ([...arr], {...obj}) to avoid shared references across tests.
• Beware of floating-point comparisons; use tolerances for numeric stability.

Practice Plan: From “I fixed it once” to “I debug under pressure”

• Week 1: Focus on crashes. Solve 5 easy/mediums where the main risk is bounds/null. Add assertions and logging to each.
• Week 2: Focus on timeouts. Take 3 problems with naive O(N^2) risk. Add loop counters, then design a brute-force comparator for tiny inputs.
• Week 3: Focus on silent wrong answers. Use problems with tricky constraints (rotated arrays, duplicates in BST). Force yourself to write invariants first.
• Week 4: Mixed drills + mock explanations. After each fix, spend 3 minutes explaining root cause aloud or to a mock interview simulator.
• Keep a weekly retro in your DSA learning path: “Bug → cause → invariant → guardrail.”

FAQ

How do I pick which tests to log?
Start with the smallest failing case, then the max constraint case. Add one “weird” case (negatives or duplicates) to expose assumptions.

Is it okay to rely on AI to find the bug?
Use AI to surface traces or suggest likely branches, but insist on verifying the fix yourself. You’ll need to explain it in interviews.

What if the code times out instead of crashing?
Log loop counts and bail out after a threshold; then compare with a brute-force version on small input to see where work explodes.

How do I debug pointer-heavy languages quickly?
Add boundary checks, assert non-null pointers, and print addresses sparingly. For C++/Java, guard your indices and prefer testable helper functions.

How do I avoid breaking other test cases after a fix?
Re-run the smallest baseline set plus one max constraint case after every change. If possible, keep a tiny regression list for each pattern (window, binary search, DFS) and rerun those too.

What if logs flood the console?
Add a step counter and early-return logs (only first 20 iterations). Log summarized state (lengths, counts) instead of full arrays when large.

Conclusion

Effective debugging is a system: reproduce tightly, log with intent, isolate branches, and test edges. Pair that with light AI assistance and you’ll fix runtime errors faster—while building the interview skill that matters most: explaining why the bug happened and why your fix is correct.