⚡️ Speed up function sorter by 173,233%

[codeflash-ai]

📄 173,233% (1,732.33x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.38 seconds → 1.95 milliseconds (best of 464 runs)

📝 Explanation and details

Here’s an optimized version of your program. The main optimization is switching from bubble sort (which is O(n²)) to Python’s built-in sort (which uses Timsort and runs in O(n log n)). The function output and logging is preserved.

This version is significantly faster and uses less memory for large lists because it eliminates unnecessary loops and swaps.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 58 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	6.97ms	17.0μs	✅40896%
test_bubble_sort.py::test_sort	823ms	138μs	✅593566%
test_bubble_sort_conditional.py::test_sort	5.88μs	3.17μs	✅85.6%
test_bubble_sort_import.py::test_sort	827ms	139μs	✅592251%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	828ms	138μs	✅599748%
test_bubble_sort_parametrized.py::test_sort_parametrized	508ms	138μs	✅368124%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	102μs	21.1μs	✅388%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large scale test data
import string  # used for string sorting 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_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 3.21μs (65.0% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.42μs -> 3.12μs (73.3% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.08μs (59.5% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.00μs (59.7% 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 # 4.25μs -> 2.96μs (43.7% faster)

def test_sorter_basic_strings():
    # Sorting strings alphabetically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.21μs (52.0% faster)

def test_sorter_basic_floats():
    # Sorting floats
    arr = [2.2, 1.1, 3.3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.67μs -> 3.54μs (60.0% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [3, -1, 2, -5, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.08μs (63.5% faster)

def test_sorter_basic_mixed_integers_floats():
    # List with both integers and floats
    arr = [1, 2.2, 0, 3.1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.38μs -> 3.62μs (75.9% faster)

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.83μs (38.2% faster)

def test_sorter_edge_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 2.92μs (35.7% faster)

def test_sorter_edge_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.17μs -> 3.00μs (38.9% faster)

def test_sorter_edge_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.92μs (32.9% faster)

def test_sorter_edge_large_negatives():
    # List with very large negative numbers
    arr = [-999999999, -1000000000, -999999998]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.17μs (51.4% faster)

def test_sorter_edge_large_positives():
    # List with very large positive numbers
    arr = [999999999, 1000000000, 999999998]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.25μs (42.3% faster)

def test_sorter_edge_case_sensitive_strings():
    # Sorting strings with different cases
    arr = ["apple", "Banana", "banana", "Apple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.25μs (57.7% faster)

def test_sorter_edge_special_characters():
    # List with special characters in strings
    arr = ["!apple", "#banana", "apple", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.25μs (48.7% faster)

def test_sorter_edge_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.17μs (57.9% faster)

def test_sorter_edge_nan_and_inf():
    # List with float('inf'), float('-inf'), and float('nan')
    arr = [3, float('inf'), -1, float('-inf'), 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.54μs -> 3.50μs (86.9% faster)

def test_sorter_edge_nan_raises():
    # List with float('nan'), which is not orderable
    arr = [1, float('nan'), 2]
    # Sorting with NaN should not raise, but NaN will always compare False, so it may end up at the end
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.21μs (44.2% faster)
    # 1 and 2 must be in order
    idx_1 = result.index(1)
    idx_2 = result.index(2)

def test_sorter_edge_mutation():
    # Ensure the input list is mutated (since the implementation is in-place)
    arr = [2, 1]
    sorter(arr) # 4.00μs -> 3.00μs (33.3% faster)

def test_sorter_edge_mixed_types_raises():
    # List with incomparable types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.88μs -> 1.96μs (46.8% faster)

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

def test_sorter_large_random_integers():
    # Sorting a large list of random integers
    arr = random.sample(range(-100000, -99000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.0ms -> 63.2μs (44169% faster)

def test_sorter_large_all_equal():
    # Large list with all elements equal
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.1ms -> 27.9μs (64568% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 29.5μs (62698% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.4ms -> 30.4μs (100010% 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 # 29.7ms -> 91.1μs (32556% 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 # 24.8ms -> 50.6μs (48888% faster)

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.5ms -> 278μs (9776% faster)

def test_sorter_large_negative_and_positive():
    # Large list with both negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.3ms -> 65.0μs (43351% 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

# unit tests

# --------------------
# BASIC TEST CASES
# --------------------

def test_sorter_basic_sorted():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.54μs -> 3.04μs (49.3% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.08μs (56.8% faster)

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.08μs (52.7% faster)

def test_sorter_basic_duplicates():
    # List with duplicates should be sorted and retain duplicates
    arr = [4, 2, 5, 2, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.04μs (54.8% faster)

def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.79μs -> 2.96μs (28.1% faster)

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.96μs (31.0% faster)

def test_sorter_basic_all_equal():
    # All elements equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.17μs -> 2.96μs (40.8% faster)

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

def test_sorter_edge_empty():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.46μs -> 2.88μs (20.3% faster)

def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.25μs (53.8% faster)

def test_sorter_edge_large_numbers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.17μs -> 3.54μs (74.1% faster)

def test_sorter_edge_floats():
    # List with floating point numbers
    arr = [2.2, 3.3, 1.1, -1.1, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.25μs -> 3.75μs (66.7% faster)

def test_sorter_edge_mixed_int_float():
    # List with both ints and floats
    arr = [1, 2.2, 0, -3.5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 3.79μs (61.6% faster)

def test_sorter_edge_strings():
    # List of strings
    arr = ['banana', 'apple', 'cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.25μs (56.4% faster)

def test_sorter_edge_strings_case():
    # List of strings with different cases
    arr = ['Banana', 'apple', 'Cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.29μs (46.9% faster)

def test_sorter_edge_single_char_strings():
    # List of single character strings
    arr = ['d', 'a', 'c', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.21μs (54.6% faster)

def test_sorter_edge_unicode_strings():
    # List with unicode strings
    arr = ['éclair', 'apple', 'Éclair', 'banana']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.92μs -> 3.62μs (63.2% faster)


def test_sorter_edge_mixed_types():
    # List with mixed types (int, str) should raise TypeError
    arr = [1, 'a', 2, 'b']
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.88μs -> 2.04μs (40.8% faster)

def test_sorter_edge_none_elements():
    # List with None and numbers should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.54μs -> 1.92μs (32.6% faster)

def test_sorter_edge_nan_inf():
    # List with NaN and inf
    arr = [float('nan'), 1, float('inf'), -float('inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.88μs -> 3.42μs (72.0% faster)

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

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.7ms -> 30.5μs (61315% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.9ms -> 30.1μs (102625% faster)

def test_sorter_large_random():
    # Large random list of integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.0ms -> 62.8μs (44436% 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 -> 51.2μs (48034% 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 # 29.7ms -> 91.5μs (32358% faster)

def test_sorter_large_floats():
    # 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 # 26.4ms -> 288μs (9063% faster)

def test_sorter_large_all_equal():
    # Large list where all elements are the same
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.3ms -> 26.9μs (67789% faster)

# --------------------
# ADDITIONAL EDGE CASES
# --------------------

def test_sorter_stability():
    # Test that the sort is stable for objects with equal keys
    class Obj:
        def __init__(self, key, val):
            self.key = key
            self.val = val
        def __lt__(self, other):
            return self.key < other.key
        def __eq__(self, other):
            return self.key == other.key and self.val == other.val
        def __repr__(self):
            return f"Obj({self.key},{self.val})"
    arr = [Obj(1, 'a'), Obj(1, 'b'), Obj(2, 'c'), Obj(2, 'd')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.54μs -> 4.21μs (79.2% faster)

def test_sorter_mutation():
    # Test that the input list is mutated (in-place sort)
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    sorter(arr) # 4.58μs -> 3.04μs (50.7% 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-mdnn7cxp and push.