⚡️ Speed up function sorter by 30,125%

[codeflash-ai]

📄 30,125% (301.25x) speedup for sorter in cli/code_to_optimize/bubble_sort.py

⏱️ Runtime : 9.03 seconds → 29.9 milliseconds (best of 32 runs)

📝 Explanation and details

The given code is implementing the bubble sort algorithm, which is not optimal for sorting. We can significantly increase the speed of this function by switching to a more efficient sorting algorithm like Timsort, which is the default sorting algorithm used in Python. Here's the optimized version.

This utilizes Python's built-in sort method, which is highly efficient with a time complexity of O(n log n).

✅ Correctness verification report:

Test	Status
⏪ Replay Tests	🔘 None Found
⚙️ Existing Unit Tests	✅ 39 Passed
🔎 Concolic Coverage Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 38 Passed
📊 Tests Coverage	No coverage data found for sorter

⚙️ Existing Unit Tests Details

- test_bubble_sort.py
- test_bubble_sort__perfinstrumented.py
- test_bubble_sort_conditional.py
- test_bubble_sort_conditional__perfinstrumented.py
- test_bubble_sort_import.py
- test_bubble_sort_import__perfinstrumented.py
- test_bubble_sort_in_class.py
- test_bubble_sort_in_class__perfinstrumented.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized__perfinstrumented.py
- test_bubble_sort_parametrized_loop.py
- test_bubble_sort_parametrized_loop__perfinstrumented.py
- test_sorter__unit_test_0.py
- test_sorter__unit_test_1.py

🌀 Generated Regression Tests Details

import pytest  # used for our unit tests
from code_to_optimize.bubble_sort import sorter

# unit tests

def test_already_sorted_list():
    # Test with a list that is already sorted
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter([10, 20, 30])

def test_reverse_sorted_list():
    # Test with a list that is sorted in reverse order
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter([30, 20, 10])

def test_unsorted_list():
    # Test with a list that is unsorted
    codeflash_output = sorter([3, 1, 4, 5, 2])
    codeflash_output = sorter([10, 5, 20, 15])

def test_single_element_list():
    # Test with a list containing a single element
    codeflash_output = sorter([1])
    codeflash_output = sorter([42])

def test_empty_list():
    # Test with an empty list
    codeflash_output = sorter([])

def test_list_with_duplicates():
    # Test with a list containing duplicate elements
    codeflash_output = sorter([2, 3, 2, 1, 3])
    codeflash_output = sorter([5, 5, 5, 5])

def test_list_with_negative_numbers():
    # Test with a list containing negative numbers
    codeflash_output = sorter([-1, -3, -2, 0, 2])
    codeflash_output = sorter([-5, -10, 0, 5, 10])

def test_list_with_mixed_numbers():
    # Test with a list containing both positive and negative numbers
    codeflash_output = sorter([-10, 5, -20, 15, 0])
    codeflash_output = sorter([3, -1, 4, -5, 2])

def test_list_with_floats():
    # Test with a list containing floating point numbers
    codeflash_output = sorter([2.5, 3.1, 1.8, 2.9])
    codeflash_output = sorter([1.1, 1.01, 1.001])

def test_list_with_strings():
    # Test with a list containing strings
    codeflash_output = sorter(["apple", "banana", "cherry"])
    codeflash_output = sorter(["dog", "cat", "bird"])

def test_large_list():
    # Test with a large list to assess performance
    large_list = list(range(1000, 0, -1))
    sorted_large_list = list(range(1, 1001))
    codeflash_output = sorter(large_list)

def test_non_comparable_elements():
    # Test with a list containing non-comparable elements
    with pytest.raises(TypeError):
        sorter([1, "two", 3])
    with pytest.raises(TypeError):
        sorter([None, 1, 2])

def test_stability():
    # Test with a list to check stability (order of equal elements)
    codeflash_output = sorter([1, 2, 2, 3, 3, 1])
    codeflash_output = sorter([5, 3, 3, 5, 1, 1])
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort import sorter

# unit tests

# Test basic functionality with sorted list
def test_sorted_list():
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter(['a', 'b', 'c'])

# Test basic functionality with reverse sorted list
def test_reverse_sorted_list():
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter(['z', 'y', 'x'])

# Test basic functionality with unsorted list
def test_unsorted_list():
    codeflash_output = sorter([3, 1, 4, 2, 5])
    codeflash_output = sorter(['b', 'a', 'd', 'c'])

# Test edge case with empty list
def test_empty_list():
    codeflash_output = sorter([])

# Test edge case with single element list
def test_single_element_list():
    codeflash_output = sorter([1])
    codeflash_output = sorter(['a'])

# Test edge case with repeated elements
def test_repeated_elements():
    codeflash_output = sorter([2, 3, 2, 1, 1])
    codeflash_output = sorter(['a', 'b', 'a', 'c', 'b'])

# Test edge case with negative numbers
def test_negative_numbers():
    codeflash_output = sorter([-1, -3, -2, 0, 2])

# Test edge case with mixed positive and negative numbers
def test_mixed_numbers():
    codeflash_output = sorter([3, -1, 2, -3, 0])

# Test special case with all identical elements
def test_identical_elements():
    codeflash_output = sorter([5, 5, 5, 5])

# Test special case with large numbers
def test_large_numbers():
    codeflash_output = sorter([1000000, 500000, 1000001])

# Test performance with large list
def test_large_list():
    large_list = list(range(1000, 0, -1))
    sorted_list = list(range(1, 1001))
    codeflash_output = sorter(large_list)

# Test robustness with non-comparable elements
def test_non_comparable_elements():
    with pytest.raises(TypeError):
        sorter([1, 'a', 3])

# Test edge case with floats
def test_floats():
    codeflash_output = sorter([3.1, 2.2, 5.5, 4.4])

# Test edge case with mixed integers and floats
def test_mixed_integers_floats():
    codeflash_output = sorter([1, 2.2, 3, 0.5])
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-sorter-maxab79k and push.

[codeflash-ai-dev]

⚠️ Suggested changes are too large to apply directly.

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T07.18.55

🔗 Compare codeflash/optimize-sorter-maxab79k → codeflash/optimize-pr277-2025-05-21T07.18.55

[codeflash-ai-dev]

⚠️ Suggested changes are too large to apply directly.

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T07.23.07

🔗 Compare codeflash/optimize-sorter-maxab79k → codeflash/optimize-pr277-2025-05-21T07.23.07

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T07.23.39

🔍 Code diff preview between branches:
codeflash/optimize-sorter-maxab79k → codeflash/optimize-pr277-2025-05-21T07.23.39

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T07.34.32

➡️ View Code Diff ➜

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T07.42.06

🔍 View Code Diff

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T07.43.43

🔍 View Code Diff Click me

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T07.50.14

 
 📦 Create Pull Request 
 

 
 🔍 View Code Diff 
 

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T07.52.47

[ 
 Title 
 ][Link]

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T07.59.02

 
📦 Create Pull Request
       
  🔍 View Code Diff
  

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T08.01.22

 
📦 Create Pull Request
       
  🔍 View Code Diff
  

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T08.02.55

 
📦 Create Pull Request
       
   
 🔍 View Code Diff  
 
  

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T08.07.20

   
   
  📦 Create Pull Request  
   
      
   
 🔍 View Code Diff  
 
  

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T08.20.16

   
   
  📦 Create Pull Request  
   
      
   
 🔍 View Code Diff  
 
  

[codeflash-ai-dev]

⚡️ Codeflash found optimizations for this PR

📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

I created a new dependent PR with the suggested changes. Please review:

• ⚡️ Speed up function sorter by 4211531.56 in PR #277 (codeflash/optimize-sorter-maxab79k) #278

If you approve, it will be merged into this PR (branch codeflash/optimize-sorter-maxab79k).

[codeflash-ai-dev]

⚡️ Codeflash found optimizations for this PR

📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

I created a new dependent PR with the suggested changes. Please review:

• ⚡️ Speed up function sorter by 4211531.56 in PR #277 (codeflash/optimize-sorter-maxab79k) #279

If you approve, it will be merged into this PR (branch codeflash/optimize-sorter-maxab79k).

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T08.32.24

• 📦 Create Pull Request

• 🔍 View Code Diff

[codeflash-ai-dev]

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T08.39.10

• 📦 Create PR from codeflash/optimize-pr277-2025-05-21T08.39.10 into codeflash/optimize-sorter-maxab79k

• 🔍 View diff between codeflash/optimize-sorter-maxab79k and codeflash/optimize-pr277-2025-05-21T08.39.10

[codeflash-ai-dev]

⚡️ Codeflash found optimizations for this PR

⚡️Codeflash found 4211531.56 (42115.32) speedup for sorter

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

📝 Explanation and details

The function you provided, sorter, is already using Python's built-in sort function which has a time complexity of O(n log n), where n is a number of elements in the array. This is the fastest achievable sorting complexity for comparison-based sorts.

However, if you want to achieve a marginal speed increase, writing this in-place might help.

Here's an alternative version using list comprehension. Although this does not improve the time complexity, it gives a Pythonic touch:

def sorter(arr):
    return sorted(arr)

Again, this command returns a new sorted list and does not modify the original list. If you want to sort the list in-place, you only have the original function:

Please note that sorting time complexity cannot be improved further than O(n log n) using comparison-based sorting algorithms. To really optimize this function, you would need a guarantee about the content of your data, for example, if your array only contained integers in a particular range, then you could use counting sort or radix sort, which can have a time complexity of O(n).

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

# 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'])

To test or edit this optimization locally git merge codeflash/optimize-pr277-2025-05-21T08.42.03

• 📦 Create PR from codeflash/optimize-pr277-2025-05-21T08.42.03 into codeflash/optimize-sorter-maxab79k

• 🔍 View diff between codeflash/optimize-sorter-maxab79k and codeflash/optimize-pr277-2025-05-21T08.42.03

[codeflash-ai-dev]

⚡️ Codeflash found optimizations for this PR

📄 4211531.56 (42115.32) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 1070554.63 → 25.42 (best of undefined runs)

I created a new dependent PR with the suggested changes. Please review:

• ⚡️ Speed up function sorter by 4211531.56 in PR #277 (codeflash/optimize-sorter-maxab79k) #280

If you approve, it will be merged into this PR (branch codeflash/optimize-sorter-maxab79k).