⚡️ Speed up function sorter by 180,478%

[codeflash-ai]

📄 180,478% (1,804.78x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.38 seconds → 1.87 milliseconds (best of 568 runs)

📝 Explanation and details

Here’s an optimized version of your program, rewritten for speed. The original used an unoptimized bubble sort (O(n²)), and iterated needlessly over the full array even when already sorted portions could be skipped.
Replacing it with Python's built-in list.sort() (which is implemented in C and uses Timsort, O(n log n)), greatly speeds up the code and reduces memory movements.
This change preserves all output and the function signature.

Notes.

• arr.sort() sorts the input list in-place and is equivalent (in result) to your previous logic.
• All print statements and returned value are untouched except for the replaced sorting part.
• No extra memory usage; even less, since no needless swap variables are created.

If you still want to use a bubble sort, it can be further optimized by stopping the sort early if no swaps occur in a pass. If you want to see that code as well, just ask! But for best speed, use arr.sort().

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 55 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
benchmarks/test_benchmark_bubble_sort.py::test_sort2	6.87ms	16.1μs	✅42643%
test_bubble_sort.py::test_sort	827ms	140μs	✅588292%
test_bubble_sort_conditional.py::test_sort	5.71μs	3.17μs	✅80.2%
test_bubble_sort_import.py::test_sort	832ms	144μs	✅575045%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	831ms	136μs	✅609379%
test_bubble_sort_parametrized.py::test_sort_parametrized	506ms	132μs	✅382348%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	101μs	21.4μs	✅373%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for testing with strings

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

# unit tests

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

def test_sorter_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.17μs (61.9% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.12μs (60.0% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.08μs (55.4% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 3.21μs (67.5% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.12μs (60.0% faster)

def test_sorter_basic_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 3.00μs (44.4% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 3.08μs (32.4% faster)

def test_sorter_basic_two_elements():
    # Two elements, unsorted
    arr = [9, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.12μs -> 3.00μs (37.5% faster)

def test_sorter_basic_two_elements_sorted():
    # Two elements, already sorted
    arr = [3, 9]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 2.96μs (36.6% faster)

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.67μs -> 2.83μs (29.4% faster)

def test_sorter_edge_large_negative_and_positive():
    # Large negative and positive numbers
    arr = [-1000, 500, 0, 999, -999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.67μs -> 3.33μs (70.0% faster)

def test_sorter_edge_floats():
    # List with floats
    arr = [3.1, 2.2, 5.5, 1.0, 4.8]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.92μs -> 3.71μs (86.5% faster)

def test_sorter_edge_mixed_int_float():
    # List with both ints and floats
    arr = [1, 3.5, 2, 4.0, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 3.58μs (67.4% faster)

def test_sorter_edge_strings():
    # List of strings (alphabetical sort)
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.12μs (54.7% faster)

def test_sorter_edge_strings_with_case():
    # List of strings with different cases
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.12μs (44.0% faster)

def test_sorter_edge_unicode_strings():
    # List of unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 3.42μs (70.8% faster)

def test_sorter_edge_already_sorted_large():
    # Already sorted large list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.4ms -> 29.5μs (62206% faster)

def test_sorter_edge_all_negative():
    # All negative numbers
    arr = [-5, -1, -3, -2, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 3.25μs (62.8% faster)

def test_sorter_edge_min_max():
    # List with min and max int values
    arr = [0, -2147483648, 2147483647, 42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.38μs (54.3% faster)

def test_sorter_edge_non_comparable_types():
    # List with non-comparable types should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.50μs -> 1.83μs (36.4% faster)

def test_sorter_edge_nested_lists():
    # List with nested lists should raise TypeError
    arr = [1, [2, 3], 4]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.54μs -> 1.83μs (38.6% faster)

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

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.3ms -> 58.0μs (48711% faster)

def test_sorter_large_random_floats():
    # Large list of random floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.4ms -> 262μs (10304% faster)

def test_sorter_large_strings():
    # Large list of random strings
    arr = [
        ''.join(random.choices(string.ascii_letters, k=10))
        for _ in range(1000)
    ]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.3ms -> 102μs (28564% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.9ms -> 48.0μs (51810% faster)

def test_sorter_large_already_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 29.1μs (63569% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.5ms -> 28.7μs (106395% faster)

def test_sorter_large_all_same():
    # Large list with all elements the same
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.2ms -> 26.0μs (69781% 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 large scale randomized tests
import string  # used for string sorting tests
import sys  # used for edge value tests

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

# unit tests

# --------------------------
# 1. BASIC TEST CASES
# --------------------------

def test_sorter_empty_list():
    # Test that empty list returns empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.04μs (52.1% faster)

def test_sorter_single_element():
    # Test that single element list returns same list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.29μs -> 3.08μs (39.2% faster)

def test_sorter_sorted_list():
    # Test that already sorted list remains unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.08μs (52.7% faster)

def test_sorter_reverse_sorted_list():
    # Test that reverse sorted list is sorted correctly
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.12μs (57.3% faster)

def test_sorter_unsorted_list():
    # Test that a typical unsorted list is sorted correctly
    arr = [3, 1, 4, 1, 5, 9, 2, 6]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 3.17μs (76.3% faster)

def test_sorter_with_duplicates():
    # Test that list with duplicate values is sorted correctly
    arr = [2, 3, 2, 1, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.12μs (58.7% faster)

def test_sorter_with_negative_numbers():
    # Test that list with negative numbers is sorted correctly
    arr = [-3, -1, -4, 2, 0, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.21μs (44.1% faster)

def test_sorter_with_all_equal_elements():
    # Test that list with all equal elements remains unchanged
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 3.17μs (25.0% faster)

def test_sorter_with_floats():
    # Test that list with floats is sorted correctly
    arr = [3.1, 2.4, -1.2, 0.0, 2.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 3.83μs (57.6% faster)

def test_sorter_with_mixed_int_float():
    # Test that list with mixed ints and floats is sorted correctly
    arr = [1, 2.2, 3, 0.5, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.79μs -> 3.71μs (83.1% faster)

# --------------------------
# 2. EDGE TEST CASES
# --------------------------

def test_sorter_large_positive_and_negative():
    # Test that list with large positive and negative numbers is sorted correctly
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 3.54μs (54.1% faster)

def test_sorter_with_min_max_and_zero():
    # Test with min, max and zero values
    arr = [0, -sys.maxsize, sys.maxsize]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 3.17μs (36.8% faster)

def test_sorter_with_strings():
    # Test that list of strings is sorted lexicographically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 3.08μs (40.5% faster)

def test_sorter_with_empty_strings():
    # Test that list with empty strings is sorted correctly
    arr = ["", "a", "abc", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.12μs (44.0% faster)

def test_sorter_with_unicode_strings():
    # Test that list with unicode strings is sorted correctly
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.75μs -> 3.38μs (70.4% faster)

def test_sorter_with_boolean_values():
    # Test that list with booleans is sorted (False < True)
    arr = [True, False, True, False]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 3.17μs (26.3% faster)

def test_sorter_with_none_raises():
    # Test that list with None raises TypeError (since None can't be compared to int/str)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.67μs -> 1.96μs (36.1% faster)

def test_sorter_with_heterogeneous_types_raises():
    # Test that list with ints and strings raises TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.54μs -> 1.88μs (35.6% faster)

def test_sorter_mutates_input():
    # Test that sorter mutates the input list (in-place sort)
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    sorter(arr) # 4.71μs -> 2.96μs (59.1% faster)

def test_sorter_returns_reference():
    # Test that the returned list is the same object as input (in-place sort)
    arr = [1, 3, 2]
    codeflash_output = sorter(arr); result = codeflash_output # 4.33μs -> 2.96μs (46.5% faster)

# --------------------------
# 3. LARGE SCALE TEST CASES
# --------------------------

def test_sorter_large_random_ints():
    # Test sorting a large list of random integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr); result = codeflash_output # 28.5ms -> 59.8μs (47533% faster)

def test_sorter_large_sorted_input():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 28.6μs (64546% faster)

def test_sorter_large_reverse_sorted_input():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.7ms -> 29.0μs (105683% faster)

def test_sorter_large_duplicates():
    # Test sorting a large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr); result = codeflash_output # 24.7ms -> 48.8μs (50534% faster)

def test_sorter_large_strings():
    # Test sorting a large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr); result = codeflash_output # 29.7ms -> 99.8μs (29643% faster)

def test_sorter_large_all_equal():
    # Test sorting a large list of all equal elements
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.2ms -> 26.6μs (68100% faster)

def test_sorter_large_floats():
    # Test sorting a large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr); result = codeflash_output # 27.7ms -> 275μs (9956% 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-mdi63t2t and push.