Completed Competitive-Coding-1

[ManasviReddy25]

No description provided.

[super30admin]

Interview Problem: Find Missing Number in a sorted array (Problem1.py)

EVALUATION

1. Correctness

The solution correctly implements a binary search algorithm to find the missing number. It uses the key insight that in a perfect sequence from 1 to n+1, arr[i] - i should always equal 1. When a number is missing, this difference becomes 2 at the boundary.

The algorithm correctly handles edge cases:

• Returns 1 if the first element isn't 1
• Returns n+1 if the last element isn't n+1
• Uses binary search to find the exact boundary where the shift occurs

Test verification:

• For [1, 2, 3, 4, 6, 7, 8]: The missing number is 5. The algorithm will find that arr[3] - 3 = 1 and arr[4] - 4 = 2, returning arr[3] + 1 = 5. ✓

2. Time Complexity

• Student's solution: O(log n) - Binary search halves the search space each iteration
• Reference solution: O(log n) - Same binary search approach
• Verdict: Equal ✓

3. Space Complexity

• Student's solution: O(1) - Only uses a fixed number of variables (lo, hi, mid, n)
• Reference solution: O(1) - Same constant space usage
• Verdict: Equal ✓

4. Code Quality

Strengths:

• Excellent inline comments explaining the algorithm logic
• Clear variable naming (lo, hi, mid are standard for binary search)
• Proper handling of edge cases at the beginning
• Clean structure with clear separation of concerns

Areas for improvement:

• The variable name n is used for len(arr) but in the problem context, n typically represents the range (1 to n), which could cause confusion. Consider renaming to length or arr_size.
• The comment # n = actual number of elements in array contradicts the problem statement where n represents the range.

5. Efficiency

The solution is already optimal:

• Binary search is the best approach for sorted arrays with this property
• No unnecessary operations or redundant checks
• Early returns for edge cases are efficient

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FEEDBACK

Strengths:

1. Excellent algorithm understanding - The student correctly identified the key insight (arr[i] - i = 1 property) and implemented binary search based on it
2. Comprehensive documentation - Comments explain not just what the code does, but why each step is necessary
3. Clean code structure - Well-organized with clear variable declarations and logical flow
4. Correct edge case handling - Properly handles missing first and last numbers

Areas for Improvement:

1. Variable naming clarification - The variable n represents array length, but in the problem context, n typically refers to the range (1 to n). Consider using length or arr_len to avoid confusion.
2. Minor inconsistency in comments - The comment says n = actual number of elements but the problem states the array contains n-1 integers from 1 to n, which could confuse readers.

Overall: This is a well-implemented solution that matches the reference approach in both correctness and efficiency.

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VERDICT: PASS

VERDICT: NEEDS_IMPROVEMENT

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Interview Problem: Design Min Heap

The student has submitted a solution for a completely different problem (Missing Number) rather than the Min Heap implementation requested. This appears to be a case of submitting the wrong solution file.

Strengths:

• Clear and well-commented code
• Correct handling of edge cases
• Efficient binary search implementation
• Good variable naming and code structure

Areas for Improvement:

• The solution does not match the problem requirements - it should implement a Min Heap, not solve the missing number problem
• If this was intentional, the student should clarify why a different problem was solved
• The solution should include the three required operations: getMin(), extractMin(), and insert()

VERDICT: NEEDS_IMPROVEMENT