⚡️ Speed up function sorter by 179,204%

[codeflash-ai]

📄 179,204% (1,792.04x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.36 seconds → 1.87 milliseconds (best of 482 runs)

📝 Explanation and details

Here is a heavily optimized version of your function.
Your code uses an O(n²) bubble sort; you can replace it with Python’s built-in, highly optimized Timsort with just one line.
All output and function signatures are preserved exactly.

Explanation:

• arr.sort() sorts the list in place in O(n log n) time, much faster than a nested loop.
• All prints and the return semantics are unchanged.
• No unnecessary array copying or loops.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 54 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details and Performance Breakdown

• benchmarks/test_benchmark_bubble_sort.py

  • test_sort2: 6.92ms -> 16.8μs $\color{green}(+410.04\%)$

• test_bubble_sort.py

  • test_sort: 833ms -> 137μs $\color{green}(+6042.42\%)$

• test_bubble_sort_conditional.py

  • test_sort: 6.21μs -> 3.04μs $\color{green}(+1.04\%)$

• test_bubble_sort_import.py

  • test_sort: 824ms -> 139μs $\color{green}(+5914.66\%)$

• test_bubble_sort_in_class.py

  • TestSorter.test_sort_in_pytest_class: 828ms -> 139μs $\color{green}(+5926.31\%)$

• test_bubble_sort_parametrized.py

  • test_sort_parametrized: 509ms -> 139μs $\color{green}(+3647.84\%)$

• test_bubble_sort_parametrized_loop.py

  • test_sort_loop_parametrized: 104μs -> 21.0μs $\color{green}(+3.99\%)$

🌀 Generated Regression Tests Details and Performance Breakdown

import random  # used for generating large random lists
import string  # used for generating lists of strings
import sys  # used for testing with very large/small numbers

# 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 # 4.54μs -> 3.08μs

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.00μs

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 2.92μs

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [4, 2, 5, 2, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 2.96μs

def test_sorter_basic_negatives():
    # List with negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.04μs

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.96μs

def test_sorter_basic_two_elements():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 2.92μs

def test_sorter_basic_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.83μs -> 2.88μs

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.67μs -> 2.79μs

def test_sorter_edge_all_equal():
    # All elements equal
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 2.96μs

def test_sorter_edge_large_numbers():
    # Very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.21μs -> 3.79μs

def test_sorter_edge_floats():
    # List with floats
    arr = [3.1, 2.2, 5.5, 1.0, 4.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.29μs -> 3.79μs

def test_sorter_edge_mixed_int_float():
    # List with mixed ints and floats
    arr = [1, 2.5, 3, 0.5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.62μs -> 3.88μs

def test_sorter_edge_strings():
    # List of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.25μs

def test_sorter_edge_unicode_strings():
    # List with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.17μs -> 3.54μs

def test_sorter_edge_custom_objects():
    # List of tuples (sort by first element)
    arr = [(3, "c"), (1, "a"), (2, "b")]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 3.58μs

def test_sorter_edge_stability():
    # Test stability: equal elements should retain original order
    class Item:
        def __init__(self, key, label):
            self.key = key
            self.label = label
        def __lt__(self, other):
            return self.key < other.key
        def __gt__(self, other):
            return self.key > other.key
        def __eq__(self, other):
            return self.key == other.key and self.label == other.label
        def __repr__(self):
            return f"Item({self.key}, '{self.label}')"
    a = Item(1, 'a')
    b = Item(2, 'b')
    c = Item(1, 'c')
    arr = [a, b, c]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.29μs -> 4.21μs

def test_sorter_edge_non_comparable():
    # List with non-comparable types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_nan_and_inf():
    # List with NaN and inf
    arr = [float('nan'), 1, float('inf'), -float('inf'), 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 3.62μs

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

def test_sorter_large_random_ints():
    # Large list of random integers
    arr = random.sample(range(-100000, -99000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.4ms -> 62.2μs

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 28.8μs

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.0ms -> 29.0μs

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 -> 52.0μs

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 # 29.8ms -> 88.7μs

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 -> 286μs

def test_sorter_large_almost_sorted():
    # Large list that's almost sorted except for a few elements
    arr = list(range(1000))
    arr[500], arr[501] = arr[501], arr[500]
    arr[998], arr[999] = arr[999], arr[998]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.4ms -> 29.1μs
# 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 min/max int 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 # 4.54μs -> 3.08μs

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 # 4.96μs -> 3.04μs

def test_sorter_unsorted_list():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.04μs

def test_sorter_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.00μs

def test_sorter_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.42μs -> 2.96μs

def test_sorter_single_element():
    # Single-element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 2.96μs

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.54μs -> 2.75μs

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

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

def test_sorter_mixed_sign_numbers():
    # List with both positive and negative numbers
    arr = [5, -10, 0, 3, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.12μs

def test_sorter_large_and_small_integers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999, -999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 3.33μs

def test_sorter_floats():
    # List with floats
    arr = [3.1, 2.2, 5.5, 1.0, 4.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 3.50μs

def test_sorter_floats_and_integers():
    # List with both floats and integers
    arr = [3, 2.2, 5, 1.1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 3.62μs

def test_sorter_strings():
    # List of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.17μs

def test_sorter_strings_with_case():
    # List of strings with mixed case
    arr = ["Banana", "apple", "Cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.21μs

def test_sorter_unicode_strings():
    # List of unicode strings
    arr = ["café", "apple", "banana", "ångström"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 3.42μs

def test_sorter_empty_strings():
    # List with empty strings
    arr = ["", "a", "abc", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.21μs


def test_sorter_mixed_types_error():
    # List with mixed types (e.g., int and str) should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_none_elements_error():
    # List containing None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

# -------------------------------
# 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 # 18.3ms -> 30.0μs

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 # 30.7ms -> 29.9μs

def test_sorter_large_random_integers():
    # Large random list of integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.1ms -> 62.8μs

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 # 25.0ms -> 51.1μs

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 -> 91.7μs

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.4ms -> 281μs

def test_sorter_large_all_equal():
    # Large list where all elements are equal
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.2ms -> 26.7μs

# -------------------------------
# Additional Edge/Mutation-Resistant Cases
# -------------------------------

def test_sorter_stability():
    # Test stability: elements with equal keys retain their relative order
    class StableObj:
        def __init__(self, key, value):
            self.key = key
            self.value = value
        def __lt__(self, other):
            return self.key < other.key
        def __gt__(self, other):
            return self.key > other.key
        def __eq__(self, other):
            return self.key == other.key
        def __repr__(self):
            return f"({self.key},{self.value})"
    arr = [StableObj(1, 'a'), StableObj(1, 'b'), StableObj(2, 'c')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.29μs -> 4.17μs

def test_sorter_mutation_does_not_return_new_object():
    # Test that the function sorts in-place and returns the same list object
    arr = [2, 1, 3]
    arr_id_before = id(arr)
    codeflash_output = sorter(arr); result = codeflash_output # 4.38μs -> 3.04μs
    arr_id_after = id(result)

def test_sorter_input_not_modified_on_copy():
    # Test that passing a copy does not modify the original
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    sorter(arr_copy)
# 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-mc13jx5v and push.