⚡️ Speed up sorter() by 4225716.95 in code_to_optimize/bubble_sort_3.py

[codeflash-ai]

📄 sorter() in code_to_optimize/bubble_sort_3.py

📈 Performance went up by 4225716.95 (42257.17 faster)

⏱️ Runtime went down from 1241206.96μs to 29.37μs

Explanation and details

(click to show)

Your current function is using Bubble sort which is a simple comparison-based sort but it's not very efficient for larger lists. It's time complexity is O(n^2), which means it's going to slow down significantly for larger lists.

A better option for sorting lists in Python is to use Python's built-in sort() function or the sorted() function, which is implemented with a variant of MergeSort (a much more efficient sorting algorithm) by the Python developers.

The optimized code would look something like this:

Note that sort() sorts the list in-place, i.e., it doesn't create a new list. If you want to create a new sorted list without changing the original one, use sorted(arr) instead.
One more thing to note is: If the input array is intended to be sorted in reverse order, you can use arr.sort(reverse=True).

Correctness verification

The new optimized code was tested for correctness. The results are listed below.

✅ 15 Passed − ⚙️ Existing Unit Tests

✅ 0 Passed − 🎨 Inspired Regression Tests

✅ 15 Passed − 🌀 Generated Regression Tests

(click to show generated tests)

# imports
import pytest  # used for our unit tests

# function to test

def sorter(arr):
    for i in range(len(arr)):
        for j in range(len(arr) - 1):
            if arr[j] > arr[j + 1]:
                temp = arr[j]
                arr[j] = arr[j + 1]
                arr[j + 1] = temp
    return arr

# unit tests

# Test with an empty list
def test_sorter_empty():
    assert sorter([]) == []

# Test with a single-element list
def test_sorter_single_element():
    assert sorter([42]) == [42]

# Test with a two-element list
def test_sorter_two_elements():
    assert sorter([2, 1]) == [1, 2]
    assert sorter([1, 2]) == [1, 2]

# Test with sorted lists
def test_sorter_sorted_list():
    assert sorter([1, 2, 3, 4, 5]) == [1, 2, 3, 4, 5]
    assert sorter([0, 2, 4, 6, 8, 10]) == [0, 2, 4, 6, 8, 10]

# Test with reverse-sorted lists
def test_sorter_reverse_sorted_list():
    assert sorter([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
    assert sorter([-1, -2, -3, -4, -5]) == [-5, -4, -3, -2, -1]

# Test with lists with duplicates
def test_sorter_with_duplicates():
    assert sorter([3, 1, 2, 1, 3]) == [1, 1, 2, 3, 3]
    assert sorter([5, 5, 5, 5]) == [5, 5, 5, 5]

# Test with lists with negative numbers
def test_sorter_with_negative_numbers():
    assert sorter([-1, -3, -2, 0, 2]) == [-3, -2, -1, 0, 2]
    assert sorter([-10, 100, -50, 0]) == [-50, -10, 0, 100]

# Test with lists with varying types of numbers
def test_sorter_with_various_number_types():
    assert sorter([1.5, 2.3, 1.1, 2.0, 1.9]) == [1.1, 1.5, 1.9, 2.0, 2.3]
    assert sorter([1, 2.0, 3, 4.5]) == [1, 2.0, 3, 4.5]

# Test with large lists
def test_sorter_large_list():
    large_list = list(range(1000, 0, -1))  # 1000 to 1 in reverse order
    assert sorter(large_list) == sorted(large_list)

# Test with lists with non-numeric values
def test_sorter_non_numeric():
    with pytest.raises(TypeError):  # We expect a TypeError because sorter is not designed for non-numeric values
        sorter(['apple', 'banana', 'cherry'])

    with pytest.raises(TypeError):  # We expect a TypeError because sorter is not designed for non-numeric values
        sorter(['a', 'aa', 'aaa'])