⚡️ Speed up function sorter by 289%

[codeflash-ai]

📄 289% (2.89x) speedup for sorter in code_to_optimize/bubble_sort_3.py

⏱️ Runtime : 933 milliseconds → 240 milliseconds (best of 34 runs)

📝 Explanation and details

Here’s a much faster and more efficient implementation using Python’s built-in sorting, which internally uses Timsort (much faster than bubble sort).
Built-ins are highly optimized and less memory intensive for this problem.

If it is required to sort in-place, which is sometimes important (modifying the input array instead of returning a new one).

Both options will return exactly the same output as before, but are much, much faster.

If you require a handwritten, faster-than-bubble-sort algorithm
(while still using only basic methods), an in-place insertion sort is better.

But the previous two methods using .sort() or sorted() are preferred for both performance and simplicity.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 52 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random
import sys

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort_3 import sorter

# unit tests

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

def test_empty_list():
    # Sorting an empty list should return an empty list
    codeflash_output = sorter([]) # 1.33μs -> 1.43μs (7.12% slower)

def test_single_element_list():
    # Sorting a single-element list should return the same list
    codeflash_output = sorter([5]) # 1.40μs -> 1.06μs (32.1% faster)

def test_sorted_list():
    # Sorting an already sorted list should return the same list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 3.12μs -> 2.03μs (53.4% faster)

def test_reverse_sorted_list():
    # Sorting a reverse-sorted list should return a sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 3.79μs -> 2.79μs (35.5% faster)

def test_unsorted_list():
    # Sorting a randomly unsorted list should return a sorted list
    codeflash_output = sorter([3, 1, 4, 5, 2]) # 3.58μs -> 2.40μs (49.0% faster)

def test_list_with_duplicates():
    # Sorting a list with duplicate values should preserve duplicates in order
    codeflash_output = sorter([3, 1, 2, 3, 2]) # 3.54μs -> 2.46μs (44.0% faster)

def test_list_with_negative_numbers():
    # Sorting a list with negative and positive numbers
    codeflash_output = sorter([0, -1, 5, -10, 3]) # 3.94μs -> 2.70μs (45.9% faster)

def test_list_with_all_equal_elements():
    # Sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7]) # 2.47μs -> 1.78μs (38.9% faster)

def test_list_with_floats_and_integers():
    # Sorting a list with both floats and integers
    codeflash_output = sorter([3.5, 2, 1.1, 2.0]) # 3.79μs -> 2.82μs (34.4% faster)

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

def test_list_with_large_and_small_numbers():
    # Sorting a 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) # 4.37μs -> 2.94μs (48.4% faster)

def test_list_with_min_max_int():
    # Sorting a list with min and max int values
    arr = [0, -2147483648, 2147483647]
    expected = [-2147483648, 0, 2147483647]
    codeflash_output = sorter(arr) # 2.60μs -> 1.95μs (33.0% faster)

def test_list_with_nan_and_infinity():
    # Sorting a list with float('nan'), float('inf'), and float('-inf')
    arr = [float('nan'), 2, float('inf'), -3, float('-inf'), 0]
    # NaN is always placed at the end in Python's sorted(), so we mimic that
    sorted_arr = sorted(arr, key=lambda x: (isinstance(x, float) and (x != x), x))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.33μs -> 3.55μs (50.2% faster)

def test_list_with_strings_raises():
    # Sorting a list with non-comparable types (e.g., int and str) should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr) # 3.27μs -> 2.92μs (12.0% faster)

def test_list_with_none_raises():
    # Sorting a list with None and numbers should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr) # 3.13μs -> 2.91μs (7.74% faster)

def test_list_with_mutable_elements():
    # Sorting a list of lists should sort by first element of each sublist
    arr = [[3, 4], [1, 2], [2, 3]]
    expected = [[1, 2], [2, 3], [3, 4]]
    codeflash_output = sorter(arr) # 2.83μs -> 2.25μs (26.1% faster)

def test_list_with_empty_lists():
    # Sorting a list containing empty lists and non-empty lists
    arr = [[1], [], [0], []]
    expected = [[], [], [0], [1]]
    codeflash_output = sorter(arr) # 3.37μs -> 2.64μs (27.8% faster)

def test_list_with_boolean_values():
    # Sorting a list with booleans and integers (True==1, False==0)
    arr = [True, False, 2, 1]
    expected = [False, True, 1, 2]
    codeflash_output = sorter(arr) # 2.95μs -> 2.13μs (38.4% faster)

def test_list_is_sorted_in_place():
    # The function should mutate the input list in place
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 2.50μs -> 1.96μs (27.5% faster)

def test_list_with_custom_objects_raises():
    # Sorting a list with objects that do not support comparison should raise TypeError
    class NoCompare:
        pass
    arr = [NoCompare(), NoCompare()]
    with pytest.raises(TypeError):
        sorter(arr) # 3.02μs -> 2.84μs (6.38% faster)

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

def test_large_random_list():
    # Sorting a large list of random integers
    arr = [random.randint(-100000, 100000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 81.5ms -> 21.0ms (287% faster)

def test_large_sorted_list():
    # Sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 58.8ms -> 100μs (58497% faster)

def test_large_reverse_sorted_list():
    # Sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 83.9ms -> 38.8ms (116% faster)

def test_large_list_with_duplicates():
    # 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()) # 70.1ms -> 15.9ms (340% faster)

def test_large_list_with_floats():
    # Sorting a large list with floats and integers
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 77.5ms -> 20.4ms (280% faster)

def test_large_list_with_negatives_and_positives():
    # Sorting a large list with both negative and positive numbers
    arr = [random.randint(-1000000, 1000000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 80.6ms -> 20.7ms (290% 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 non-integer test cases
import sys  # used for maxsize/minsize edge cases

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort_3 import sorter

# unit tests

# ------------------------------
# 1. Basic Test Cases
# ------------------------------

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 1.11μs -> 1.18μs (6.17% slower)

def test_sorter_single_element():
    # Test sorting a list with one element
    codeflash_output = sorter([42]) # 1.32μs -> 1.00μs (31.4% faster)

def test_sorter_sorted_list():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 2.71μs -> 1.80μs (50.6% faster)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 3.45μs -> 2.50μs (38.0% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 5, 2]) # 3.14μs -> 2.14μs (46.9% faster)

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = sorter([4, 2, 5, 2, 3, 2]) # 3.70μs -> 2.41μs (53.6% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-1, -3, 2, 0, -2]) # 3.19μs -> 2.22μs (43.5% faster)

def test_sorter_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([0, -10, 5, -7, 3]) # 3.51μs -> 2.31μs (52.3% faster)

def test_sorter_all_equal_elements():
    # Test sorting a list where all elements are equal
    codeflash_output = sorter([7, 7, 7, 7]) # 2.30μs -> 1.64μs (40.1% faster)

def test_sorter_two_elements_sorted():
    # Test sorting a two-element list that is already sorted
    codeflash_output = sorter([1, 2]) # 1.69μs -> 1.36μs (24.5% faster)

def test_sorter_two_elements_unsorted():
    # Test sorting a two-element list that is not sorted
    codeflash_output = sorter([2, 1]) # 1.89μs -> 1.53μs (23.8% faster)

# ------------------------------
# 2. Edge Test Cases
# ------------------------------

def test_sorter_large_and_small_integers():
    # Test sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 1, -1]
    expected = [-sys.maxsize - 1, -1, 0, 1, sys.maxsize]
    codeflash_output = sorter(arr) # 4.05μs -> 2.74μs (47.8% faster)

def test_sorter_floats_and_integers():
    # Test sorting a list with floats and integers
    arr = [1.1, 2, 0.5, -3.2, 2.0]
    expected = [-3.2, 0.5, 1.1, 2, 2.0]
    codeflash_output = sorter(arr) # 4.49μs -> 3.11μs (44.4% faster)

def test_sorter_negative_zero():
    # Test sorting a list with -0.0 and 0.0
    arr = [-0.0, 0.0, 1, -1]
    # -0.0 == 0.0, but -1 < -0.0 == 0.0 < 1
    expected = [-1, -0.0, 0.0, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 3.51μs -> 2.49μs (41.1% faster)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(arr) # 2.46μs -> 1.97μs (24.6% faster)

def test_sorter_mixed_types_raises():
    # Test sorting a list with mixed types (should raise TypeError)
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr) # 3.10μs -> 2.85μs (8.81% faster)


def test_sorter_none_in_list_raises():
    # Test sorting a list containing None (should raise TypeError)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr) # 3.63μs -> 3.13μs (15.9% faster)

def test_sorter_unicode_and_ascii_strings():
    # Test sorting strings with unicode and ascii
    arr = ["z", "ä", "a", "ü"]
    expected = ["a", "z", "ä", "ü"]  # Python sorts ASCII before unicode
    codeflash_output = sorter(arr) # 3.46μs -> 2.43μs (42.6% faster)

def test_sorter_boolean_values():
    # Test sorting a list of booleans (False < True)
    arr = [True, False, True, False]
    expected = [False, False, True, True]
    codeflash_output = sorter(arr) # 3.25μs -> 2.28μs (42.4% faster)

def test_sorter_large_identical_elements():
    # Test sorting a list with a large number of identical elements
    arr = [5] * 100
    expected = [5] * 100
    codeflash_output = sorter(arr) # 340μs -> 7.41μs (4489% faster)

def test_sorter_immutable_input():
    # Test that the function does not return a new list (sorts in-place)
    arr = [3, 1, 2]
    codeflash_output = sorter(arr); result = codeflash_output # 2.39μs -> 1.77μs (34.8% faster)

# ------------------------------
# 3. Large Scale Test Cases
# ------------------------------

def test_sorter_large_random_list():
    # Test sorting a large list of random integers
    arr = [random.randint(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 80.0ms -> 20.8ms (284% faster)

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 58.8ms -> 102μs (57294% faster)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 90.4ms -> 38.9ms (133% 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(arr.copy()) # 72.6ms -> 16.0ms (355% faster)

def test_sorter_large_floats():
    # Test sorting a large list of floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 77.5ms -> 19.3ms (301% faster)

def test_sorter_large_strings():
    # Test sorting a large list of random lowercase strings
    arr = [''.join(random.choices(string.ascii_lowercase, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 100ms -> 27.7ms (264% 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-md8wi5zp and push.