⚡️ Speed up function sorter by 151,054%

[codeflash-ai]

📄 151,054% (1,510.54x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.80 seconds → 2.51 milliseconds (best of 113 runs)

📝 Explanation and details

Here’s the optimized rewrite, switching from O(N²) inefficient bubble sort to fast built-in Timsort (used by Python’s sort).
All logic and output are preserved.
No changes to function signature or variable names.

The entire nested loop is replaced with arr.sort(), which is much faster and more memory-efficient for all list sizes.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 57 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	7.72ms	22.2μs	✅34659%
test_bubble_sort.py::test_sort	952ms	158μs	✅600283%
test_bubble_sort_conditional.py::test_sort	11.8μs	8.67μs	✅36.6%
test_bubble_sort_import.py::test_sort	930ms	156μs	✅592984%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	943ms	158μs	✅595792%
test_bubble_sort_parametrized.py::test_sort_parametrized	560ms	161μs	✅346437%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	140μs	49.9μs	✅181%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for max int value

# 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 # 9.54μs -> 8.21μs (16.2% faster)

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 8.33μs (34.5% faster)

def test_sorter_unsorted_list():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 7.96μs (35.6% faster)

def test_sorter_with_duplicates():
    # List with duplicate values
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 8.25μs (37.4% faster)

def test_sorter_with_negative_numbers():
    # List containing negative numbers
    arr = [0, -1, -3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 8.17μs (33.2% faster)

def test_sorter_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.67μs -> 8.08μs (19.6% faster)

def test_sorter_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.88μs -> 7.92μs (24.7% faster)

def test_sorter_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.96μs -> 7.88μs (26.5% faster)

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.54μs -> 8.00μs (19.3% faster)

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

def test_sorter_all_equal_elements():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 8.04μs (29.0% faster)

def test_sorter_large_and_small_numbers():
    # List with very large and very small integers
    arr = [0, sys.maxsize, -sys.maxsize - 1, 42, -1000, 1000]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.8μs -> 8.42μs (51.5% faster)
    expected = sorted(arr)

def test_sorter_floats():
    # List with float numbers
    arr = [3.14, 2.71, -1.0, 0.0, 2.71]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.3μs -> 9.54μs (28.8% faster)

def test_sorter_mixed_int_float():
    # List with mixed integers and floats
    arr = [1, 2.5, 0, -1.5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 9.25μs (32.0% faster)

def test_sorter_strings():
    # List of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.62μs (27.0% faster)

def test_sorter_single_character_strings():
    # List of single character strings
    arr = ["z", "a", "b", "y"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.38μs (29.4% faster)

def test_sorter_empty_strings():
    # List with empty strings
    arr = ["", "a", "", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.46μs (29.5% faster)

def test_sorter_unicode_strings():
    # List with unicode characters
    arr = ["α", "β", "γ", "δ", "α"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.8μs -> 9.00μs (41.7% faster)

def test_sorter_list_with_none():
    # List containing None should raise TypeError in Python 3 when compared
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.4μs -> 41.7μs (4.00% faster)

def test_sorter_mixed_types():
    # List with mixed types (e.g., int and str) should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.2μs -> 42.2μs (2.47% faster)

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

def test_sorter_large_random_list():
    # Large random list of integers (size 1000)
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.6ms -> 73.2μs (41713% faster)

def test_sorter_large_sorted_list():
    # Large already sorted list (size 1000)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.7ms -> 36.5μs (56737% faster)

def test_sorter_large_reverse_sorted_list():
    # Large reverse sorted list (size 1000)
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.7ms -> 36.6μs (94654% 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 # 28.4ms -> 58.1μs (48718% 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 # 32.6ms -> 99.2μs (32721% faster)

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 # 30.2ms -> 287μs (10418% faster)

# -------------------------
# Additional Edge Cases
# -------------------------

def test_sorter_stability():
    # The sort should be stable: equal elements retain their relative order
    class Item:
        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 and self.value == other.value
        def __repr__(self):
            return f"Item({self.key}, {self.value})"

    arr = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c'), Item(2, 'd')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.1μs -> 14.9μs (35.3% faster)

def test_sorter_custom_objects_without_comparison():
    # Objects without comparison methods should raise TypeError
    class NoCompare:
        pass
    arr = [NoCompare(), NoCompare()]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.6μs -> 42.2μs (3.26% 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 testing lists of strings

# 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]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 11.4μs -> 8.29μs (37.7% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 11.3μs -> 8.08μs (40.2% faster)

def test_sorter_basic_unsorted():
    # Unsorted list with positive integers
    arr = [3, 1, 4, 5, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 11.0μs -> 8.08μs (36.6% faster)

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 3]
    expected = [1, 2, 2, 3, 3]
    codeflash_output = sorter(arr.copy()) # 10.9μs -> 8.12μs (34.4% faster)

def test_sorter_basic_negative_numbers():
    # List with negative and positive numbers
    arr = [-1, -3, 2, 0, 1]
    expected = [-3, -1, 0, 1, 2]
    codeflash_output = sorter(arr.copy()) # 11.0μs -> 8.33μs (31.5% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()) # 9.96μs -> 8.00μs (24.5% faster)

def test_sorter_basic_two_elements():
    # List with two elements, unsorted
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 9.92μs -> 8.08μs (22.7% faster)

def test_sorter_basic_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7]
    expected = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()) # 10.6μs -> 8.08μs (31.4% faster)

def test_sorter_basic_floats():
    # List with float values
    arr = [3.1, 2.4, 5.6, 1.0]
    expected = [1.0, 2.4, 3.1, 5.6]
    codeflash_output = sorter(arr.copy()) # 12.1μs -> 8.58μs (41.3% faster)

def test_sorter_basic_mixed_int_float():
    # List with both int and float values
    arr = [2, 3.5, 1, 2.2]
    expected = [1, 2, 2.2, 3.5]
    codeflash_output = sorter(arr.copy()) # 11.7μs -> 9.33μs (25.4% faster)

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()) # 9.54μs -> 7.88μs (21.2% faster)

def test_sorter_edge_large_negative():
    # List with large negative and positive numbers
    arr = [999999999, -1000000000, 0, 123456789, -1]
    expected = [-1000000000, -1, 0, 123456789, 999999999]
    codeflash_output = sorter(arr.copy()) # 12.3μs -> 8.42μs (46.5% faster)

def test_sorter_edge_strings():
    # List of strings (should sort lexicographically)
    arr = ["banana", "apple", "cherry", "date"]
    expected = ["apple", "banana", "cherry", "date"]
    codeflash_output = sorter(arr.copy()) # 11.5μs -> 9.42μs (22.6% faster)

def test_sorter_edge_strings_with_empty():
    # List of strings with empty string
    arr = ["", "b", "a"]
    expected = ["", "a", "b"]
    codeflash_output = sorter(arr.copy()) # 10.8μs -> 8.29μs (30.6% faster)

def test_sorter_edge_case_sensitive_strings():
    # List of strings with mixed case (should sort by ASCII)
    arr = ["apple", "Banana", "banana", "Apple"]
    expected = ["Apple", "Banana", "apple", "banana"]
    codeflash_output = sorter(arr.copy()) # 10.9μs -> 8.38μs (30.4% faster)

def test_sorter_edge_unicode_strings():
    # List of unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    expected = ["Éclair", "apple", "banana", "éclair"]
    codeflash_output = sorter(arr.copy()) # 11.8μs -> 9.21μs (28.1% faster)

def test_sorter_edge_bool_values():
    # List with boolean values (False < True)
    arr = [True, False, True, False]
    expected = [False, False, True, True]
    codeflash_output = sorter(arr.copy()) # 11.0μs -> 8.50μs (28.9% faster)

def test_sorter_edge_none_values():
    # List containing None should raise TypeError
    arr = [1, None, 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 45.5μs -> 41.7μs (9.19% faster)

def test_sorter_edge_incomparable_types():
    # List with incomparable types should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.6μs -> 41.8μs (4.29% faster)

def test_sorter_edge_nested_lists():
    # List of lists (should sort by first element of each sublist)
    arr = [[2, 3], [1, 2], [2, 2]]
    expected = [[1, 2], [2, 2], [2, 3]]
    codeflash_output = sorter(arr.copy()) # 11.2μs -> 8.62μs (30.0% faster)

def test_sorter_edge_large_numbers():
    # List with very large numbers
    arr = [10**18, 10**10, -10**12, 0]
    expected = [-10**12, 0, 10**10, 10**18]
    codeflash_output = sorter(arr.copy()) # 11.5μs -> 8.75μs (31.4% faster)

def test_sorter_edge_mutability():
    # Ensure the function sorts in-place and returns the same object
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 10.4μs -> 8.12μs (27.7% faster)

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

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 30.9ms -> 71.5μs (43118% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 21.4ms -> 35.7μs (59731% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 34.6ms -> 35.3μs (97974% 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()) # 28.5ms -> 56.6μs (50214% 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()) # 33.1ms -> 97.5μs (33813% 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()) # 30.5ms -> 289μs (10425% faster)

def test_sorter_large_all_equal():
    # Large list where all elements are equal
    arr = [7] * 1000
    expected = [7] * 1000
    codeflash_output = sorter(arr.copy()) # 20.6ms -> 32.3μs (63562% faster)

def test_sorter_large_alternating():
    # Large list with alternating values
    arr = [1, 2] * 500
    expected = [1] * 500 + [2] * 500
    codeflash_output = sorter(arr.copy()) # 25.0ms -> 50.7μs (49247% 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-mdnnm0k8 and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #555 by misrasaurabh1 was closed.