⚡️ Speed up function sorter by 29,075%

[codeflash-ai]

📄 29,075% (290.75x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 443 milliseconds → 1.52 milliseconds (best of 439 runs)

📝 Explanation and details

Here’s how your program can be greatly accelerated.

• The original code uses bubble sort, which is O(n²) and extremely slow for anything but tiny arrays.
• Python provides much faster sort mechanisms (Timsort, O(n log n)), and using the built-in list.sort() is C speed and memory-efficient (in-place).
• Directly sum and return, as you already do.

Code comments are retained, as requested.

Here’s the optimized code.

Performance:

• Asymptotic improvement from O(n²) to O(n log n).
• No extra memory allocation (sort in-place).
• Maintains your print statements and return as before.

Let me know if you want to also preserve the original array order or any other subtle behaviour!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 19 Passed
🌀 Generated Regression Tests	✅ 47 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_bubble_sort.py::test_sort	9.12ms	43.0μs	✅21140%
test_bubble_sort_conditional.py::test_sort	7.04μs	4.75μs	✅48.3%
test_bubble_sort_import.py::test_sort	9.09ms	41.6μs	✅21742%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	9.09ms	41.2μs	✅21977%
test_bubble_sort_parametrized.py::test_sort_parametrized	5.73ms	41.5μs	✅13687%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	135μs	34.2μs	✅297%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import sys  # used for maxsize edge cases

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

# unit tests

# -----------------------
# 1. Basic Test Cases
# -----------------------

def test_sorted_list():
    # Already sorted list should remain unchanged, sum of first 3 elements is correct
    arr = [1, 2, 3, 4, 5]
    sorted_arr, summed = sorter(arr.copy()) # 8.50μs -> 4.67μs (82.1% faster)

def test_reverse_sorted_list():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    sorted_arr, summed = sorter(arr.copy()) # 7.88μs -> 4.79μs (64.3% faster)

def test_unsorted_list():
    # Random order list should be sorted and sum calculated
    arr = [3, 1, 4, 5, 2]
    sorted_arr, summed = sorter(arr.copy()) # 6.62μs -> 4.71μs (40.7% faster)

def test_list_with_duplicates():
    # Duplicates should be sorted correctly and sum calculated
    arr = [2, 3, 2, 1, 3]
    sorted_arr, summed = sorter(arr.copy()) # 6.62μs -> 4.50μs (47.2% faster)

def test_list_with_negatives():
    # Negative numbers should be sorted before positives
    arr = [0, -1, 3, -2, 2]
    sorted_arr, summed = sorter(arr.copy()) # 6.83μs -> 4.71μs (45.1% faster)

def test_list_with_single_element():
    # Single element list should return itself and sum should be that element
    arr = [42]
    sorted_arr, summed = sorter(arr.copy()) # 5.08μs -> 4.50μs (13.0% faster)

def test_list_with_two_elements():
    # Two elements should be sorted and sum is sum of both
    arr = [9, 3]
    sorted_arr, summed = sorter(arr.copy()) # 5.58μs -> 4.33μs (28.8% faster)

def test_list_with_three_elements():
    # Three elements, unsorted
    arr = [7, 2, 5]
    sorted_arr, summed = sorter(arr.copy()) # 5.54μs -> 4.71μs (17.7% faster)

# -----------------------
# 2. Edge Test Cases
# -----------------------

def test_empty_list():
    # Empty list should return empty list and sum 0
    arr = []
    sorted_arr, summed = sorter(arr.copy()) # 4.50μs -> 4.33μs (3.85% faster)

def test_list_with_all_same_elements():
    # All elements same, should remain same and sum = 3 * element (if len >= 3)
    arr = [5, 5, 5, 5, 5]
    sorted_arr, summed = sorter(arr.copy()) # 5.62μs -> 4.79μs (17.4% faster)

def test_list_with_less_than_three_elements():
    # Only two elements, sum should be sum of both
    arr = [10, 20]
    sorted_arr, summed = sorter(arr.copy()) # 5.04μs -> 4.83μs (4.32% faster)

def test_list_with_one_element():
    # Only one element, sum should be that element
    arr = [99]
    sorted_arr, summed = sorter(arr.copy()) # 4.62μs -> 4.42μs (4.71% faster)

def test_list_with_min_max_int():
    # List contains sys.maxsize and -sys.maxsize-1
    arr = [sys.maxsize, 0, -sys.maxsize-1]
    sorted_arr, summed = sorter(arr.copy()) # 6.21μs -> 4.83μs (28.5% faster)

def test_list_with_floats_and_ints():
    # List contains both floats and ints
    arr = [1.5, 2, -3.7, 0]
    sorted_arr, summed = sorter(arr.copy()) # 8.29μs -> 6.25μs (32.7% faster)

def test_list_with_large_negative_and_positive():
    # Large positive and negative numbers
    arr = [999999999, -999999999, 0]
    sorted_arr, summed = sorter(arr.copy()) # 6.75μs -> 4.83μs (39.6% faster)

def test_list_with_zeros():
    # List with only zeros
    arr = [0, 0, 0]
    sorted_arr, summed = sorter(arr.copy()) # 6.58μs -> 4.71μs (39.8% faster)

def test_list_with_mixed_types_error():
    # List with non-numeric types should raise TypeError
    arr = [1, 'a', 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 5.00μs -> 3.71μs (34.8% faster)

def test_list_with_nan_and_inf():
    # List with float('nan') and float('inf')
    arr = [1, float('nan'), float('inf'), -1]
    sorted_arr, summed = sorter(arr.copy()) # 8.17μs -> 5.17μs (58.1% faster)
    # Remove nan and check the rest is sorted
    arr_wo_nan = [x for x in sorted_arr if not (isinstance(x, float) and x != x)]
    # summed may be nan if nan is in first 3, or otherwise sum of first 3 numbers
    if any(isinstance(x, float) and x != x for x in sorted_arr[:3]):
        pass
    else:
        pass

# -----------------------
# 3. Large Scale Test Cases
# -----------------------

def test_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    sorted_arr, summed = sorter(arr.copy()) # 26.0ms -> 58.2μs (44546% faster)

def test_large_reverse_sorted_list():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    sorted_arr, summed = sorter(arr.copy()) # 41.7ms -> 58.6μs (71052% faster)

def test_large_random_list():
    # Large random list
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    sorted_arr, summed = sorter(arr.copy()) # 38.5ms -> 124μs (30827% faster)

def test_large_list_with_duplicates():
    # Large list with many duplicates
    arr = [5] * 1000
    sorted_arr, summed = sorter(arr.copy()) # 25.7ms -> 55.0μs (46575% faster)

def test_large_list_with_negatives_and_positives():
    # Large list with both negative and positive numbers
    arr = [i if i % 2 == 0 else -i for i in range(1000)]
    expected = sorted(arr)
    sorted_arr, summed = sorter(arr.copy()) # 34.1ms -> 80.5μs (42262% faster)

def test_large_list_with_floats():
    # Large list with floats
    arr = [float(i) / 10 for i in range(1000)]
    random.shuffle(arr)
    expected = sorted(arr)
    sorted_arr, summed = sorter(arr.copy()) # 36.6ms -> 226μs (16106% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import random  # used for generating large scale random lists
import sys  # used for edge case testing with sys.maxsize

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

# unit tests

# ---------------- Basic Test Cases ----------------

def test_sorter_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    sorted_arr, summed = sorter(arr.copy()) # 6.29μs -> 4.88μs (29.1% faster)

def test_sorter_reverse_sorted():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    sorted_arr, summed = sorter(arr.copy()) # 6.50μs -> 4.79μs (35.7% faster)

def test_sorter_unsorted_list():
    # Random unsorted list
    arr = [3, 1, 4, 2, 5]
    sorted_arr, summed = sorter(arr.copy()) # 6.00μs -> 4.83μs (24.1% faster)

def test_sorter_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 3]
    sorted_arr, summed = sorter(arr.copy()) # 6.00μs -> 4.79μs (25.2% faster)

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-1, -3, 2, 0, 1]
    sorted_arr, summed = sorter(arr.copy()) # 6.12μs -> 4.79μs (27.8% faster)

def test_sorter_single_element():
    # List with a single element
    arr = [42]
    sorted_arr, summed = sorter(arr.copy()) # 5.00μs -> 4.62μs (8.11% faster)

def test_sorter_two_elements():
    # List with two elements, unsorted
    arr = [5, 3]
    sorted_arr, summed = sorter(arr.copy()) # 5.12μs -> 4.38μs (17.1% faster)

def test_sorter_three_elements():
    # List with exactly three elements
    arr = [7, 2, 5]
    sorted_arr, summed = sorter(arr.copy()) # 5.42μs -> 4.54μs (19.2% faster)

# ---------------- Edge Test Cases ----------------

def test_sorter_empty_list():
    # Empty list should return empty and sum 0
    arr = []
    sorted_arr, summed = sorter(arr.copy()) # 4.58μs -> 4.08μs (12.2% faster)

def test_sorter_all_equal_elements():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    sorted_arr, summed = sorter(arr.copy()) # 5.62μs -> 4.54μs (23.9% faster)

def test_sorter_fewer_than_three_elements():
    # List with two elements
    arr = [8, 3]
    sorted_arr, summed = sorter(arr.copy()) # 5.21μs -> 4.46μs (16.8% faster)

    # List with one element
    arr = [9]
    sorted_arr, summed = sorter(arr.copy()) # 4.67μs -> 4.08μs (14.3% faster)

def test_sorter_large_and_small_integers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    sorted_arr, summed = sorter(arr.copy()) # 7.38μs -> 5.29μs (39.4% faster)

def test_sorter_already_sorted_short():
    # Already sorted short list
    arr = [1, 2]
    sorted_arr, summed = sorter(arr.copy()) # 5.17μs -> 4.54μs (13.8% faster)

def test_sorter_negative_and_positive_mix():
    # Mix of negative and positive, with duplicates
    arr = [-2, -2, 0, 3, -1, 3]
    sorted_arr, summed = sorter(arr.copy()) # 6.38μs -> 4.96μs (28.6% faster)

def test_sorter_list_with_zeroes():
    # List with several zeroes
    arr = [0, 0, 0, 1, -1]
    sorted_arr, summed = sorter(arr.copy()) # 6.92μs -> 4.62μs (49.5% faster)

def test_sorter_mutation():
    # Ensure the input list is mutated (in-place sort)
    arr = [4, 1, 3]
    arr_copy = arr.copy()
    sorter(arr) # 6.54μs -> 4.46μs (46.7% faster)

# ---------------- Large Scale Test Cases ----------------

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    sorted_arr, summed = sorter(arr.copy()) # 25.9ms -> 59.5μs (43412% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    sorted_arr, summed = sorter(arr.copy()) # 41.6ms -> 58.6μs (70908% faster)

def test_sorter_large_random():
    # Large random list
    arr = random.sample(range(-10000, -9000), 1000)
    arr_copy = arr.copy()
    sorted_arr, summed = sorter(arr_copy) # 38.4ms -> 126μs (30247% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [42] * 1000
    sorted_arr, summed = sorter(arr.copy()) # 25.7ms -> 55.5μs (46261% faster)

def test_sorter_large_negatives():
    # Large list of negative numbers
    arr = [-(i + 1) for i in range(1000)]
    random.shuffle(arr)
    arr_copy = arr.copy()
    sorted_arr, summed = sorter(arr_copy) # 38.0ms -> 124μs (30468% faster)

def test_sorter_large_mixed():
    # Large list with a mix of negative, zero, and positive
    arr = [i - 500 for i in range(1000)]  # -500 to 499
    random.shuffle(arr)
    arr_copy = arr.copy()
    sorted_arr, summed = sorter(arr_copy) # 37.5ms -> 122μs (30659% faster)
# 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-md43medi and push.