⚡️ Speed up function sorter by 194,594%

[codeflash-ai]

📄 194,594% (1,945.94x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.35 seconds → 1.72 milliseconds (best of 468 runs)

📝 Explanation and details

REFINEMENT Here is an optimized version of your program. The provided implementation uses an inefficient bubble sort (O(n²)). Python's built-in sort method is much faster (Timsort, O(n log n)) and sorts in place.
All outputs are preserved and the return value remains the same.

This will run dramatically faster and is also less memory intensive.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 56 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	6.86ms	16.5μs	✅41394%
test_bubble_sort.py::test_sort	816ms	138μs	✅588723%
test_bubble_sort_conditional.py::test_sort	6.38μs	3.08μs	✅107%
test_bubble_sort_import.py::test_sort	823ms	138μs	✅592726%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	823ms	140μs	✅586805%
test_bubble_sort_parametrized.py::test_sort_parametrized	501ms	138μs	✅360465%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	99.3μs	21.7μs	✅357%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for maxsize in edge cases

# 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_integers():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 5.17μs -> 3.21μs (61.1% faster)

def test_sorter_reverse_sorted_integers():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 5.33μs -> 3.04μs (75.4% faster)

def test_sorter_unsorted_integers():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 4.88μs -> 3.04μs (60.3% faster)

def test_sorter_duplicates():
    # List with duplicates
    arr = [4, 2, 2, 1, 3, 4]
    expected = [1, 2, 2, 3, 4, 4]
    codeflash_output = sorter(arr.copy()) # 5.00μs -> 3.04μs (64.4% faster)

def test_sorter_negative_integers():
    # List with negative numbers
    arr = [-3, -1, -4, 2, 0]
    expected = [-4, -3, -1, 0, 2]
    codeflash_output = sorter(arr.copy()) # 4.83μs -> 3.17μs (52.6% faster)

def test_sorter_floats():
    # List with floats
    arr = [3.1, 2.4, 5.6, 1.2]
    expected = [1.2, 2.4, 3.1, 5.6]
    codeflash_output = sorter(arr.copy()) # 6.04μs -> 3.67μs (64.8% faster)

def test_sorter_mixed_integers_floats():
    # List with mixed integers and floats
    arr = [1, 2.2, 0, -1.1, 3]
    expected = [-1.1, 0, 1, 2.2, 3]
    codeflash_output = sorter(arr.copy()) # 6.25μs -> 3.79μs (64.9% faster)

def test_sorter_strings():
    # List of strings
    arr = ['banana', 'apple', 'cherry']
    expected = ['apple', 'banana', 'cherry']
    codeflash_output = sorter(arr.copy()) # 4.92μs -> 3.25μs (51.3% faster)

def test_sorter_single_element():
    # Single element list
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()) # 3.92μs -> 3.04μs (28.8% faster)

def test_sorter_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 4.21μs -> 3.00μs (40.3% faster)

def test_sorter_two_elements_sorted():
    # Two element list, already sorted
    arr = [1, 2]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 4.08μs -> 2.92μs (40.0% faster)

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

def test_sorter_empty_list():
    # Empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()) # 3.58μs -> 2.79μs (28.4% faster)

def test_sorter_all_same_elements():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    expected = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()) # 4.42μs -> 3.04μs (45.2% faster)

def test_sorter_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    expected = [-sys.maxsize-1, -999999999, 0, 999999999, sys.maxsize]
    codeflash_output = sorter(arr.copy()) # 6.29μs -> 3.54μs (77.6% faster)

def test_sorter_strings_case_sensitive():
    # Sorting is case-sensitive in Python
    arr = ['apple', 'Banana', 'banana', 'Apple']
    expected = ['Apple', 'Banana', 'apple', 'banana']
    codeflash_output = sorter(arr.copy()) # 4.62μs -> 3.29μs (40.5% faster)

def test_sorter_unicode_strings():
    # Unicode strings
    arr = ['á', 'a', 'ä', 'â']
    expected = ['a', 'â', 'ä', 'á']
    codeflash_output = sorter(arr.copy()) # 5.46μs -> 3.58μs (52.4% faster)

def test_sorter_mutates_input():
    # Ensure input list is mutated (in-place sort)
    arr = [3, 2, 1]
    sorter(arr) # 4.54μs -> 3.04μs (49.3% faster)

def test_sorter_empty_string_elements():
    # List with empty strings and normal strings
    arr = ["", "a", "b", ""]
    expected = ["", "", "a", "b"]
    codeflash_output = sorter(arr.copy()) # 4.83μs -> 3.17μs (52.7% faster)

def test_sorter_negative_zero():
    # List with -0.0 and 0.0 (should be treated as equal in Python)
    arr = [0.0, -0.0, 1.0, -1.0]
    expected = [-1.0, -0.0, 0.0, 1.0]  # -0.0 == 0.0, but -0.0 < 0.0 is False
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 3.46μs (55.4% faster)

def test_sorter_none_elements_raises():
    # List with None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.12μs -> 1.92μs (63.0% faster)

def test_sorter_heterogeneous_types_raises():
    # List with different types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.50μs -> 1.92μs (30.4% faster)


def test_sorter_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 18.1ms -> 29.4μs (61649% faster)

def test_sorter_large_reverse_sorted_list():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 30.5ms -> 29.3μs (103827% faster)

def test_sorter_large_random_list():
    # Large random list of integers
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 28.6ms -> 63.0μs (45347% faster)

def test_sorter_large_random_floats():
    # Large random list of floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.0ms -> 283μs (9412% 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()) # 29.4ms -> 102μs (28536% 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()) # 24.8ms -> 49.9μs (49497% faster)

def test_sorter_large_all_same():
    # Large list, all elements are the same
    arr = [7] * 1000
    expected = [7] * 1000
    codeflash_output = sorter(arr.copy()) # 17.7ms -> 26.9μs (65749% faster)

def test_sorter_large_already_sorted_strings():
    # Large already sorted string list
    arr = [f"{i:04d}" for i in range(1000)]
    expected = arr.copy()
    codeflash_output = sorter(arr.copy()) # 21.6ms -> 34.3μs (62704% faster)

def test_sorter_large_unicode_strings():
    # Large list of unicode strings
    base = ['á', 'a', 'ä', 'â']
    arr = [random.choice(base) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 25.6ms -> 76.1μs (33561% 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 random lists
import string  # used for string sorting tests
import sys  # used for maxsize in edge cases

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

# ========================
# Basic Test Cases
# ========================

def test_sorter_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 2.96μs (56.3% faster)

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.04μs (58.9% faster)

def test_sorter_unsorted_list():
    # Unsorted list with random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.42μs -> 3.04μs (45.2% faster)

def test_sorter_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.08μs (44.6% faster)

def test_sorter_negative_numbers():
    # List with negative and positive numbers
    arr = [0, -1, 5, -10, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 3.12μs (72.0% faster)

def test_sorter_single_element():
    # List with only one element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 2.96μs (33.8% faster)

def test_sorter_two_elements():
    # List with two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.62μs -> 2.92μs (24.3% faster)

def test_sorter_all_equal():
    # List where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.96μs (32.4% faster)

def test_sorter_floats():
    # List with float values
    arr = [2.3, 1.1, 3.3, 2.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.79μs (36.3% faster)

def test_sorter_mixed_int_float():
    # List with both int and float values
    arr = [1, 2.2, 3, 0.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 3.71μs (64.0% faster)

# ========================
# Edge Test Cases
# ========================

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.58μs -> 2.88μs (24.6% faster)

def test_sorter_large_numbers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.21μs (48.0% faster)

def test_sorter_strings():
    # List of strings should be sorted lexicographically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.21μs (50.7% faster)

def test_sorter_strings_with_case():
    # Case-sensitive string sorting
    arr = ["Banana", "apple", "Apple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.25μs (44.9% faster)

def test_sorter_unicode_strings():
    # Unicode strings should be sorted correctly
    arr = ["ápple", "apple", "äpple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 3.54μs (54.1% faster)

def test_sorter_already_sorted_large_range():
    # Already sorted list with a large range of values
    arr = list(range(-500, 500))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.3ms -> 29.6μs (61807% faster)

def test_sorter_single_char_strings():
    # List of single-character strings
    arr = list("zyxwvutsrqponmlkjihgfedcba")
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 26.7μs -> 4.00μs (567% faster)

def test_sorter_with_none_raises():
    # List with None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.83μs -> 1.92μs (47.8% faster)

def test_sorter_incomparable_types_raises():
    # List with incomparable types should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.62μs -> 1.88μs (40.0% faster)


def test_sorter_mutates_input():
    # The function should mutate the input list in-place
    arr = [3, 2, 1]
    sorter(arr) # 5.04μs -> 3.21μs (57.1% faster)

# ========================
# Large Scale Test Cases
# ========================

def test_sorter_large_random_ints():
    # 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 # 28.2ms -> 65.4μs (43067% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.3ms -> 29.3μs (62296% 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 # 30.7ms -> 29.0μs (105907% 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 # 24.6ms -> 50.9μs (48256% faster)

def test_sorter_large_strings():
    # 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 # 29.9ms -> 95.0μs (31309% 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-mddkkqoy and push.