⚡️ Speed up function sorter by 206,648%

[codeflash-ai]

📄 206,648% (2,066.48x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.36 seconds → 1.63 milliseconds (best of 587 runs)

📝 Explanation and details

Here is a rewritten, much faster version of your sorter program while preserving all required function signatures, behavior, and comments.
Main Changes.

• Uses Python's Fast C-optimized in-place .sort() instead of bubble sort, which is much faster.
• Keeps the print statements and return value unchanged.

Why this is so much faster.

• The original code is an O(n²) bubble sort: extremely slow for large lists.
• Python's .sort() (Timsort) is O(n log n), implemented in C, and is highly optimized.

All original I/O and result behavior is preserved; only the sorting algorithm has changed for speed.

✅ 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 ⏱️	Improvement
benchmarks/test_benchmark_bubble_sort.py::test_sort2	6.98ms	16.7μs	✅41689.62%
test_bubble_sort.py::test_sort	829ms	142μs	✅584070.45%
test_bubble_sort_conditional.py::test_sort	5.58μs	3.08μs	✅81.09%
test_bubble_sort_import.py::test_sort	831ms	140μs	✅590736.78%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	835ms	141μs	✅591728.25%
test_bubble_sort_parametrized.py::test_sort_parametrized	508ms	142μs	✅357061.89%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	104μs	21.0μs	✅400.02%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for edge numeric values

# 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 sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.54μs -> 2.88μs (23.20%)

def test_sorter_single_element():
    # Test sorting a list with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.71μs -> 2.96μs (25.35%)

def test_sorter_two_elements_sorted():
    # Test sorting a two-element list that is already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.96μs (31.00%)

def test_sorter_two_elements_unsorted():
    # Test sorting a two-element list that is not sorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.83μs -> 2.92μs (31.45%)

def test_sorter_multiple_elements_sorted():
    # Test sorting a list that is already sorted
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 3.00μs (44.43%)

def test_sorter_multiple_elements_reverse():
    # Test sorting a list that is in reverse order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.00μs (63.90%)

def test_sorter_multiple_elements_random():
    # Test sorting a list with random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.04μs (54.77%)

def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.04μs (53.42%)

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

def test_sorter_all_equal():
    # Test sorting a list where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.17μs -> 2.96μs (40.87%)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-3, -1, -2, -5, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.08μs (59.40%)

def test_sorter_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    arr = [-2, 3, 1, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 2.92μs (64.33%)

def test_sorter_large_and_small_numbers():
    # Test sorting a list with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 3.29μs (83.54%)

def test_sorter_floats():
    # Test sorting a list with floating point numbers
    arr = [3.14, 2.71, 0.0, -1.0, 2.71]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.96μs -> 3.75μs (58.88%)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.17μs (51.28%)

def test_sorter_strings_case_sensitive():
    # Test sorting strings with different cases
    arr = ["Banana", "apple", "Cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.17μs (51.36%)

def test_sorter_empty_strings():
    # Test sorting a list with empty strings
    arr = ["", "a", "abc", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.17μs (60.50%)

def test_sorter_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.67μs -> 3.62μs (56.33%)

def test_sorter_mutation():
    # Test that the original list is mutated (in-place sort)
    arr = [3, 1, 2]
    sorter(arr)

def test_sorter_none_in_list():
    # Test sorting a list with None values should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_heterogeneous_types():
    # Test sorting a list with mixed types should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

# ---------------------- #
# 3. LARGE SCALE TESTS   #
# ---------------------- #

def test_sorter_large_random_list():
    # Test sorting a large random list of integers (up to 1000 elements)
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.6ms -> 63.5μs (44935.57%)

def test_sorter_large_duplicates():
    # Test sorting a large list with many duplicate values
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 25.3ms -> 48.5μs (52028.30%)

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)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.4ms -> 88.0μs (34382.36%)

def test_sorter_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.7ms -> 29.6μs (63047.87%)

def test_sorter_large_reverse_sorted():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.9ms -> 29.4μs (104935.04%)
# 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 random lists
import string  # used for generating random 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_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.88μs (30.43%)

def test_sorter_single_element():
    # Test sorting a list with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.79μs -> 2.96μs (28.19%)

def test_sorter_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.04μs (47.93%)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.00μs (65.30%)

def test_sorter_unsorted_list():
    # Test sorting a randomly unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.04μs (68.53%)

def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 2.92μs (67.12%)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.08μs (56.76%)

def test_sorter_mixed_signs():
    # Test sorting a list with positive, negative, and zero
    arr = [-10, 0, 5, -3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.08μs (60.85%)

def test_sorter_floats():
    # Test sorting a list with floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.75μs -> 3.50μs (64.29%)

def test_sorter_mixed_ints_floats():
    # Test sorting a list with both ints and floats
    arr = [3, 2.2, 5, 1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 3.50μs (59.54%)

# -------------------- EDGE TEST CASES --------------------

def test_sorter_all_equal_elements():
    # Test sorting a list where all elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.29μs -> 2.88μs (49.25%)

def test_sorter_two_elements_sorted():
    # Test sorting a list with two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 2.92μs (37.17%)

def test_sorter_two_elements_unsorted():
    # Test sorting a list with two elements unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.83μs (38.26%)

def test_sorter_large_negative_and_positive():
    # Test sorting a list with large negative and positive numbers
    arr = [-1000000, 500, 0, 1000000, -500]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.75μs -> 3.42μs (68.33%)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.12μs (60.00%)

def test_sorter_mixed_case_strings():
    # Test sorting a list of strings with mixed case
    arr = ["banana", "Apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.00μs (50.00%)

def test_sorter_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ["café", "cafe", "cafè"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.58μs (44.21%)

def test_sorter_already_sorted_large_identical_prefix():
    # Test sorting a list with many elements sharing a prefix
    arr = ["aa", "ab", "ac", "ad"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.21μs (45.43%)

def test_sorter_custom_objects_raises():
    # Test sorting a list of objects that do not support comparison
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_none_in_list_raises():
    # Test sorting a list containing None and other types
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_mutates_input():
    # Test that the input list is mutated (sorter sorts in place)
    arr = [2, 1]
    sorter(arr)

def test_sorter_returns_same_object():
    # Test that the returned list is the same object as input
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 4.33μs -> 3.00μs (44.47%)

# -------------------- LARGE SCALE TEST CASES --------------------

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list (performance and correctness)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.6ms -> 29.9μs (62147.49%)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.7ms -> 29.5μs (103826.19%)

def test_sorter_large_random_list():
    # Test sorting a large randomly shuffled list
    arr = list(range(1000))
    random.seed(42)
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.6ms -> 67.7μs (40730.77%)

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)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 25.1ms -> 49.1μs (51071.76%)

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)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.3ms -> 88.0μs (34346.79%)

def test_sorter_large_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 26.7ms -> 290μs (9087.98%)

def test_sorter_large_already_sorted_strings():
    # Test sorting a large already sorted list of strings
    arr = [f"str{str(i).zfill(4)}" for i in range(1000)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.2ms -> 40.2μs (60208.38%)

def test_sorter_large_reverse_sorted_strings():
    # Test sorting a large reverse sorted list of strings
    arr = [f"str{str(i).zfill(4)}" for i in range(999, -1, -1)]
    expected = [f"str{str(i).zfill(4)}" for i in range(1000)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 36.0ms -> 41.5μs (86541.41%)
# 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-mc1b2bp0 and push.