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⚡️ Speed up function sorter by 4211531.56 in PR #107 (temp) #248

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5 changes: 2 additions & 3 deletions bubble_sort.py
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Original file line number Diff line number Diff line change
@@ -1,9 +1,8 @@
def sorter(arr):
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Suggested change
def sorter(arr):
if arr[j] > arr[j + 1]:

unused_var = None
for i in range(len(arr)):
for j in range(len(arr) - 1):
if arr[j] > arr[j + 1]:
if arr[j] > arr[j - 1]:
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Suggested change
if arr[j] > arr[j - 1]:
if arr[j] > arr[j + 1]:

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⚡️ 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)

📝 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 ✅ 39 Passed
🌀 Generated Regression Tests ✅ 38 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage undefined
⚙️ 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 
# 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-pr248-2025-03-25T18.39.05

Suggested change
if arr[j] > arr[j - 1]:
if arr[j] > arr[j + 1]:

Copy link
Author

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

⏱️ 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 ✅ 39 Passed
🌀 Generated Regression Tests ✅ 38 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage undefined
⚙️ 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 
# 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-pr248-2025-03-25T21.02.43

Suggested change
if arr[j] > arr[j - 1]:
if arr[j] > arr[j + 1]:

Copy link
Author

@codeflash-ai-dev codeflash-ai-dev bot Mar 25, 2025 •
edited by Saga4
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⚡️Codeflash found 4211531.56 (42115.32x) 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 ✅ 39 Passed
🌀 Generated Regression Tests ✅ 38 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage undefined
⚙️ 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 
# 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-pr248-2025-03-25T21.42.06

Suggested change
if arr[j] > arr[j - 1]:
if arr[j] > arr[j + 1]:

temp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = temp
arr[j - 1] = temp
return arr
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