⚡️ Speed up function sorter by 145,431%

[codeflash-ai]

📄 145,431% (1,454.31x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.41 seconds → 2.35 milliseconds (best of 464 runs)

📝 Explanation and details

Here’s a rewritten, much faster version of your program using Python’s built-in sort (Timsort), which is highly optimized, while preserving all comments and function signatures. We also maintain the print statements but minimize extra computation.

Explanation of improvements.

• Replaces the O(n²) bubble sort with built-in O(n log n) Timsort (arr.sort()), which is highly optimized in C.
• Eliminates unnecessary manual swaps and nested loops.
• All comments and function/print signatures are preserved to ensure output and interface remain unchanged.

This version will be orders of magnitude faster for any list of reasonable size.

If you must implement a sort yourself, you can use an in-place quicksort or another more efficient algorithm, but for practical, production-quality Python code, arr.sort() is fastest.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 63 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	7.07ms	16.5μs	✅42620%
test_bubble_sort.py::test_sort	828ms	142μs	✅583072%
test_bubble_sort_conditional.py::test_sort	6.88μs	3.17μs	✅117%
test_bubble_sort_import.py::test_sort	828ms	141μs	✅586021%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	829ms	143μs	✅578233%
test_bubble_sort_parametrized.py::test_sort_parametrized	500ms	141μs	✅353630%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	102μs	21.2μs	✅382%

🌀 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_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 5.46μs -> 3.12μs (74.7% faster)

def test_sorter_reverse_sorted():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 5.38μs -> 3.04μs (76.8% faster)

def test_sorter_random_order():
    # Random order list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 5.04μs -> 3.04μs (65.8% faster)

def test_sorter_with_duplicates():
    # List with duplicate values
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 4.71μs -> 3.04μs (54.8% faster)

def test_sorter_single_element():
    # List with only one element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 4.04μs -> 2.96μs (36.6% faster)

def test_sorter_two_elements_sorted():
    # List with two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 4.00μs -> 2.92μs (37.1% faster)

def test_sorter_two_elements_unsorted():
    # List with two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 3.92μs -> 2.88μs (36.2% faster)

def test_sorter_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 4.29μs -> 3.00μs (43.1% faster)

# --- Edge Test Cases ---

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 3.62μs -> 2.83μs (28.0% faster)

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 4.96μs -> 3.12μs (58.7% faster)

def test_sorter_mixed_positive_negative():
    # List with both positive and negative numbers
    arr = [3, -1, 2, -5, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 5.21μs -> 3.12μs (66.7% faster)

def test_sorter_large_numbers():
    # Test with very large numbers
    arr = [sys.maxsize, -sys.maxsize, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 6.42μs -> 3.50μs (83.3% faster)

def test_sorter_floats():
    # List with floating point numbers
    arr = [3.2, 1.5, 4.8, 2.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 5.96μs -> 3.62μs (64.4% faster)

def test_sorter_mixed_int_float():
    # List with both integers and floats
    arr = [3, 1.1, 2, 4.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 5.42μs -> 3.54μs (52.9% faster)

def test_sorter_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 4.88μs -> 3.12μs (56.0% faster)

def test_sorter_strings_with_uppercase():
    # List of strings with uppercase and lowercase
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 4.54μs -> 3.04μs (49.4% faster)

def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["α", "β", "γ", "δ"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 5.12μs -> 3.71μs (38.2% faster)

def test_sorter_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 5.08μs -> 3.12μs (62.7% faster)

def test_sorter_list_with_none_raises():
    # List with None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())  # 3.04μs -> 1.92μs (58.8% faster)

def test_sorter_incomparable_types_raises():
    # List with incomparable types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())  # 2.50μs -> 1.88μs (33.3% faster)

# --- Large Scale Test Cases ---

def test_sorter_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 18.4ms -> 30.5μs (60223% faster)

def test_sorter_large_reverse_sorted_list():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 30.9ms -> 29.3μs (105453% faster)

def test_sorter_large_random_list():
    # Large random list
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 28.5ms -> 60.0μs (47292% 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 -> 50.2μs (48963% 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  # 30.0ms -> 91.6μs (32628% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output  # 26.1ms -> 288μs (8958% faster)

# --- Mutability/Reference Tests ---

def test_sorter_returns_same_object():
    # sorter should return the same list object (in-place sort)
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output  # 5.33μs -> 3.17μs (68.4% faster)

def test_sorter_does_not_return_new_list():
    # If a new list is returned, this test will fail
    arr = [2, 1]
    codeflash_output = sorter(arr); result = codeflash_output  # 4.38μs -> 2.92μs (50.0% faster)
    arr[0] = 100  # mutate original, should reflect in result

# --- Determinism Test ---

def test_sorter_is_deterministic():
    # Multiple sorts of the same input should always yield the same output
    arr = [random.randint(-100, 100) for _ in range(50)]
    arr_copy1 = arr.copy()
    arr_copy2 = arr.copy()
    codeflash_output = sorter(arr_copy1); out1 = codeflash_output  # 60.3μs -> 6.12μs (885% faster)
    codeflash_output = sorter(arr_copy2); out2 = codeflash_output  # 59.8μs -> 5.46μs (995% 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 test data
import string  # used for string sorting tests
import sys  # used for 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_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([])  # 4.46μs -> 3.00μs (48.6% faster)

def test_sorter_single_element():
    # Test sorting a list with a single element
    codeflash_output = sorter([42])  # 3.75μs -> 2.92μs (28.6% faster)

def test_sorter_already_sorted():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5])  # 4.33μs -> 3.00μs (44.5% faster)

def test_sorter_reverse_sorted():
    # Test sorting a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1])  # 4.71μs -> 3.04μs (54.8% faster)

def test_sorter_unsorted_integers():
    # Test sorting a typical unsorted list of integers
    codeflash_output = sorter([3, 1, 4, 1, 5, 9, 2])  # 5.00μs -> 3.17μs (57.9% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate values
    codeflash_output = sorter([2, 3, 2, 1, 3, 1])  # 4.54μs -> 3.00μs (51.4% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -2, 0, 2, 1])  # 4.25μs -> 3.12μs (36.0% faster)

def test_sorter_mixed_sign_numbers():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([0, -10, 5, -2, 3])  # 4.42μs -> 3.08μs (43.3% faster)

def test_sorter_floats():
    # Test sorting a list with floating point numbers
    codeflash_output = sorter([3.1, 2.2, 5.5, 4.4])  # 5.75μs -> 3.50μs (64.3% faster)

def test_sorter_mixed_int_float():
    # Test sorting a list with both integers and floats
    codeflash_output = sorter([1, 3.5, 2, 2.5])  # 4.83μs -> 3.42μs (41.5% faster)

def test_sorter_strings():
    # Test sorting a list of strings
    codeflash_output = sorter(['banana', 'apple', 'cherry'])  # 4.38μs -> 3.04μs (43.8% faster)

def test_sorter_mixed_case_strings():
    # Test sorting a list of strings with mixed case
    codeflash_output = sorter(['Banana', 'apple', 'Cherry'])  # 3.96μs -> 3.04μs (30.1% faster)

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

def test_sorter_all_equal():
    # Test sorting a list where all elements are equal
    codeflash_output = sorter([7, 7, 7, 7])  # 3.96μs -> 3.04μs (30.2% faster)

def test_sorter_two_elements_sorted():
    # Test sorting a list with two elements already sorted
    codeflash_output = sorter([1, 2])  # 3.42μs -> 2.88μs (18.8% faster)

def test_sorter_two_elements_unsorted():
    # Test sorting a list with two elements unsorted
    codeflash_output = sorter([2, 1])  # 3.46μs -> 2.88μs (20.3% faster)

def test_sorter_large_and_small_numbers():
    # Test sorting a list with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    expected = sorted(arr)
    codeflash_output = sorter(arr)  # 5.46μs -> 3.21μs (70.1% faster)

def test_sorter_with_nan_and_inf():
    # Test sorting a list with float('nan'), float('inf'), and float('-inf')
    arr = [float('nan'), float('inf'), float('-inf'), 0, 1]
    # NaNs always compare false, so their position is implementation defined
    # We'll check that all non-nan values are sorted, and nan is at the end
    codeflash_output = sorter(arr); result = codeflash_output  # 5.33μs -> 3.54μs (50.6% faster)
    non_nan = [x for x in result if x == x]

def test_sorter_strings_with_empty():
    # Test sorting a list of strings with an empty string
    arr = ["", "a", "abc", " "]
    expected = sorted(arr)
    codeflash_output = sorter(arr)  # 4.38μs -> 3.08μs (41.9% faster)

def test_sorter_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    expected = sorted(arr)
    codeflash_output = sorter(arr)  # 6.33μs -> 3.58μs (76.8% faster)

def test_sorter_list_of_lists():
    # Test sorting a list of lists (lexicographical order)
    arr = [[3, 2], [1, 2], [1, 1], [2, 2]]
    expected = sorted(arr)
    codeflash_output = sorter(arr)  # 5.62μs -> 3.62μs (55.2% faster)

def test_sorter_list_of_tuples():
    # Test sorting a list of tuples
    arr = [(2, 3), (1, 2), (2, 2), (1, 1)]
    expected = sorted(arr)
    codeflash_output = sorter(arr)  # 5.83μs -> 3.50μs (66.7% faster)

def test_sorter_mutates_input():
    # Test that the input list is mutated (in-place sort)
    arr = [3, 2, 1]
    sorter(arr)  # 4.25μs -> 2.96μs (43.6% faster)

def test_sorter_type_error_on_incomparable():
    # Test that sorting a list with incomparable types raises TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr)  # 2.83μs -> 1.79μs (58.1% faster)

def test_sorter_none_in_list():
    # Test that sorting a list with None and ints raises TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr)  # 2.54μs -> 1.83μs (38.5% faster)

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

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = random.sample(range(-100000, -99000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(list(arr))  # 28.2ms -> 61.9μs (45514% faster)

def test_sorter_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(list(arr))  # 18.4ms -> 29.6μs (62241% faster)

def test_sorter_large_reverse_sorted():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(list(arr))  # 30.6ms -> 29.9μs (102405% 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)]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr))  # 24.7ms -> 50.4μs (49018% 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)]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr))  # 29.9ms -> 90.6μs (32926% faster)

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(list(arr))  # 26.6ms -> 288μs (9131% faster)

def test_sorter_large_negative_numbers():
    # Test sorting a large list of negative numbers
    arr = [random.randint(-1000000, -1) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr))  # 27.8ms -> 66.9μs (41433% faster)

def test_sorter_large_list_of_lists():
    # Test sorting a large list of lists (lexicographical order)
    arr = [[random.randint(0, 10), random.randint(0, 10)] for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter([list(x) for x in arr])  # 34.0ms -> 223μs (15140% faster)

def test_sorter_large_list_of_tuples():
    # Test sorting a large list of tuples
    arr = [(random.randint(0, 1000), random.randint(0, 1000)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr))  # 40.7ms -> 194μs (20799% 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-mcy3nqrg and push.