⚡️ Speed up function mysorter by 88,562%

[codeflash-ai]

📄 88,562% (885.62x) speedup for mysorter in codeflash/bubble_sort.py

⏱️ Runtime : 1.97 seconds → 2.22 milliseconds (best of 470 runs)

📝 Explanation and details

The optimization replaces the inefficient O(n²) bubble sort algorithm with Python's built-in arr.sort() method, which uses Timsort - a highly optimized O(n log n) sorting algorithm.

Key changes:

• Removed the nested loops that performed bubble sort comparisons and swaps
• Replaced with a single arr.sort() call that sorts the array in-place
• Preserved all print statements and function behavior

Why this is faster:
The original bubble sort performs up to n² comparisons and swaps, making it extremely slow for larger datasets. The profiler shows 30+ million operations in the inner loops. Python's Timsort is specifically designed to be fast on real-world data patterns - it's adaptive (faster on partially sorted data), stable, and uses sophisticated techniques like galloping mode and merge optimization.

Performance characteristics from tests:

• Small arrays (< 10 elements): 15-60% speedup due to eliminating loop overhead
• Medium arrays (100 elements): 2,000-4,000% speedup as algorithmic complexity advantage emerges
• Large arrays (1000+ elements): 40,000-97,000% speedup where the O(n log n) vs O(n²) difference becomes massive
• Best case scenarios: Already sorted or nearly sorted arrays show the highest speedups (up to 97,000%) because Timsort is adaptive and recognizes existing order

The 88,561% overall speedup demonstrates the dramatic difference between a naive O(n²) algorithm and Python's production-quality sorting implementation.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 3 Passed
🌀 Generated Regression Tests	✅ 62 Passed
⏪ Replay Tests	✅ 2 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_bubble_sort.py::test_sort	1.42s	253μs	✅559039%

🌀 Generated Regression Tests and Runtime

import random  # used to generate large random lists
import string  # used for string sorting tests
import sys  # used for large integer edge cases

# imports
import pytest  # used for our unit tests
from codeflash.bubble_sort import mysorter

# unit tests

# --------------------
# Basic Test Cases
# --------------------

def test_empty_list():
    # Test sorting an empty list
    codeflash_output = mysorter([]) # 5.21μs -> 4.00μs (30.2% faster)

def test_single_element():
    # Test sorting a list with one element
    codeflash_output = mysorter([42]) # 5.33μs -> 4.08μs (30.6% faster)

def test_two_elements_sorted():
    # Test sorting a list with two elements already sorted
    codeflash_output = mysorter([1, 2]) # 5.04μs -> 4.04μs (24.8% faster)

def test_two_elements_unsorted():
    # Test sorting a list with two elements in reverse order
    codeflash_output = mysorter([2, 1]) # 5.00μs -> 4.00μs (25.0% faster)

def test_multiple_elements_sorted():
    # Test sorting a list that is already sorted
    codeflash_output = mysorter([1, 2, 3, 4, 5]) # 5.62μs -> 4.25μs (32.4% faster)

def test_multiple_elements_reverse():
    # Test sorting a list in reverse order
    codeflash_output = mysorter([5, 4, 3, 2, 1]) # 6.25μs -> 4.08μs (53.1% faster)

def test_multiple_elements_unsorted():
    # Test sorting a list with random order
    codeflash_output = mysorter([3, 1, 4, 5, 2]) # 5.83μs -> 4.21μs (38.6% faster)

def test_with_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = mysorter([2, 3, 2, 1, 4, 1]) # 6.54μs -> 4.33μs (51.0% faster)

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

def test_all_elements_equal():
    # Test sorting a list where all elements are the same
    codeflash_output = mysorter([7, 7, 7, 7]) # 5.17μs -> 4.04μs (27.8% faster)

def test_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = mysorter([-3, -1, -2, -5, -4]) # 6.17μs -> 4.25μs (45.1% faster)

def test_mixed_positive_and_negative():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = mysorter([3, -1, 2, -5, 0]) # 6.00μs -> 4.17μs (44.0% faster)

def test_with_zeroes():
    # Test sorting a list containing zeroes
    codeflash_output = mysorter([0, 0, 1, -1, 0]) # 5.88μs -> 4.38μs (34.3% faster)

def test_large_integers():
    # Test sorting a list with very large and very small integers
    big = sys.maxsize
    small = -sys.maxsize - 1
    codeflash_output = mysorter([big, 0, small, 42]) # 6.75μs -> 4.58μs (47.3% faster)

def test_strings():
    # Test sorting a list of strings
    codeflash_output = mysorter(["banana", "apple", "cherry"]) # 5.50μs -> 4.33μs (26.9% faster)

def test_strings_with_duplicates():
    # Test sorting a list of strings with duplicates
    codeflash_output = mysorter(["apple", "banana", "apple", "cherry"]) # 5.67μs -> 4.46μs (27.1% faster)

def test_strings_case_sensitive():
    # Test sorting a list of strings with different cases (lexicographical order is case-sensitive)
    codeflash_output = mysorter(["Banana", "apple", "Cherry"]) # 5.21μs -> 4.25μs (22.5% faster)

def test_floats():
    # Test sorting a list of floats
    codeflash_output = mysorter([3.1, 2.4, -1.5, 0.0]) # 7.21μs -> 5.17μs (39.5% faster)

def test_mixed_ints_and_floats():
    # Test sorting a list with both ints and floats
    codeflash_output = mysorter([1, 2.2, 0, -3.5, 2]) # 7.88μs -> 5.00μs (57.5% faster)

def test_already_sorted_large():
    # Test sorting a larger already sorted list
    arr = list(range(100))
    codeflash_output = mysorter(arr.copy()) # 216μs -> 9.33μs (2216% faster)

def test_reverse_sorted_large():
    # Test sorting a larger reverse sorted list
    arr = list(range(99, -1, -1))
    codeflash_output = mysorter(arr.copy()) # 363μs -> 9.12μs (3883% faster)

def test_empty_string_list():
    # Test sorting a list of empty strings
    codeflash_output = mysorter(["", "", ""]) # 5.04μs -> 4.21μs (19.8% faster)

def test_list_with_none_raises():
    # Test that sorting a list with None raises a TypeError
    with pytest.raises(TypeError):
        mysorter([1, None, 2]) # 3.58μs -> 2.62μs (36.5% faster)

def test_list_with_incomparable_types_raises():
    # Test that sorting a list with incomparable types raises a TypeError
    with pytest.raises(TypeError):
        mysorter([1, "a", 2]) # 3.04μs -> 2.46μs (23.8% faster)

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

def test_large_random_integers():
    # Test sorting a large list of random integers
    arr = [random.randint(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 46.1ms -> 114μs (40186% faster)

def test_large_random_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 44.1ms -> 407μs (10725% faster)

def test_large_random_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 = mysorter(arr.copy()) # 50.7ms -> 134μs (37570% faster)

def test_large_duplicates():
    # Test sorting a large list with many duplicate values
    arr = [42] * 500 + [13] * 500
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 41.8ms -> 50.2μs (83227% faster)

def test_large_almost_sorted():
    # Test sorting a large list that is almost sorted except for a few elements
    arr = list(range(1000))
    arr[500], arr[501] = arr[501], arr[500]  # swap two elements
    arr[998], arr[999] = arr[999], arr[998]  # swap two elements at the end
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 32.0ms -> 52.1μs (61209% 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 min/max integer values

# imports
import pytest  # used for our unit tests
from codeflash.bubble_sort import mysorter

# unit tests

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

def test_empty_list():
    # Empty list should return empty list
    codeflash_output = mysorter([]) # 5.04μs -> 3.92μs (28.7% faster)

def test_single_element():
    # Single element list should return itself
    codeflash_output = mysorter([42]) # 4.83μs -> 4.17μs (16.0% faster)

def test_already_sorted():
    # Already sorted list should remain unchanged
    codeflash_output = mysorter([1, 2, 3, 4, 5]) # 5.67μs -> 4.21μs (34.6% faster)

def test_reverse_sorted():
    # Reverse sorted list should be sorted ascending
    codeflash_output = mysorter([5, 4, 3, 2, 1]) # 6.17μs -> 4.12μs (49.5% faster)

def test_unsorted_small():
    # Unsorted small list
    codeflash_output = mysorter([3, 1, 4, 2]) # 5.54μs -> 4.12μs (34.3% faster)

def test_with_duplicates():
    # List with duplicate elements
    codeflash_output = mysorter([2, 3, 2, 1, 3]) # 5.88μs -> 4.25μs (38.2% faster)

def test_negative_numbers():
    # List with negative numbers
    codeflash_output = mysorter([-1, -3, 2, 0, -2]) # 5.96μs -> 4.38μs (36.2% faster)

def test_mixed_positive_negative():
    # List with both positive and negative numbers
    codeflash_output = mysorter([4, -2, 0, -1, 3]) # 5.88μs -> 4.33μs (35.6% faster)

def test_all_equal():
    # All elements are equal
    codeflash_output = mysorter([7, 7, 7, 7]) # 5.33μs -> 4.00μs (33.3% faster)

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

def test_two_elements_sorted():
    # Two elements, already sorted
    codeflash_output = mysorter([1, 2]) # 4.67μs -> 4.04μs (15.5% faster)

def test_two_elements_unsorted():
    # Two elements, unsorted
    codeflash_output = mysorter([2, 1]) # 5.00μs -> 4.04μs (23.7% faster)

def test_large_and_small_integers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 42]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 7.33μs -> 4.54μs (61.5% faster)

def test_float_numbers():
    # List with float numbers
    arr = [3.14, 2.71, -1.0, 0.0, 2.71]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 8.21μs -> 5.29μs (55.1% faster)

def test_mixed_int_float():
    # List with both integers and floats
    arr = [1, 2.5, 0, -3.5, 2]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 7.96μs -> 4.92μs (61.8% faster)

def test_strings():
    # List of strings should sort lexicographically
    arr = ["banana", "apple", "cherry", "date"]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 6.42μs -> 4.38μs (46.7% faster)

def test_strings_with_case():
    # Strings with varying cases
    arr = ["Banana", "apple", "Cherry", "date"]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 5.79μs -> 4.29μs (35.0% faster)

def test_unicode_strings():
    # Unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 7.38μs -> 4.79μs (53.9% faster)

def test_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 6.00μs -> 4.25μs (41.2% faster)

def test_list_with_none():
    # List with None should raise TypeError (cannot compare None with int/str)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 3.33μs -> 2.62μs (27.0% faster)

def test_list_with_unorderable_types():
    # List with mixed types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 3.04μs -> 2.50μs (21.6% faster)

def test_list_of_lists():
    # List of lists should sort by lexicographical order of lists
    arr = [[2, 3], [1, 2], [2], [1, 2, 3]]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 6.92μs -> 5.00μs (38.3% faster)

def test_list_of_tuples():
    # List of tuples should sort by lexicographical order of tuples
    arr = [(2, 3), (1, 2), (2,), (1, 2, 3)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 6.92μs -> 5.12μs (35.0% faster)

def test_list_with_nan():
    # List with NaN values should sort with NaN at the end (since NaN != NaN, order may be undefined)
    import math
    arr = [1, float('nan'), 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.62μs -> 4.38μs (28.6% faster)

def test_list_with_infinity():
    # List with inf/-inf values
    arr = [1, float('inf'), -float('inf'), 0]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 6.58μs -> 4.58μs (43.6% faster)

def test_custom_objects_comparable():
    # List of custom objects with __lt__ defined
    class Box:
        def __init__(self, v): self.v = v
        def __lt__(self, other): return self.v < other.v
        def __eq__(self, other): return self.v == other.v
        def __repr__(self): return f"Box({self.v})"
    arr = [Box(3), Box(1), Box(2)]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 7.42μs -> 5.42μs (36.9% faster)

def test_custom_objects_uncomparable():
    # List of custom objects without __lt__ should raise TypeError
    class Foo:
        def __init__(self, v): self.v = v
    arr = [Foo(1), Foo(2)]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 3.04μs -> 2.58μs (17.7% faster)

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

def test_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = mysorter(arr.copy()) # 32.0ms -> 52.1μs (61287% faster)

def test_large_reverse_sorted_list():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = mysorter(arr.copy()) # 50.9ms -> 52.2μs (97329% faster)

def test_large_random_integers():
    # Large random integer list
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 46.1ms -> 116μs (39439% faster)

def test_large_random_floats():
    # Large random float list
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 44.8ms -> 410μs (10816% faster)

def test_large_strings():
    # Large list of random strings
    arr = [
        ''.join(random.choices(string.ascii_letters + string.digits, k=10))
        for _ in range(1000)
    ]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 50.5ms -> 139μs (36020% faster)

def test_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 41.8ms -> 91.0μs (45886% faster)

def test_large_all_equal():
    # Large list with all elements equal
    arr = [7] * 1000
    codeflash_output = mysorter(arr.copy()) # 31.6ms -> 49.1μs (64243% faster)

def test_large_alternating():
    # Large list alternating between two values
    arr = [0, 1] * 500
    expected = [0] * 500 + [1] * 500
    codeflash_output = mysorter(arr.copy()) # 36.7ms -> 70.9μs (51695% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

⏪ Replay Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_codeflashbubble_sort_py__replay_test_0.py::test_codeflash_bubble_sort_mysorter	7.17μs	4.46μs	✅60.8%
test_codeflashbubble_sort_py__replay_test_1.py::test_codeflash_bubble_sort_mysorter	8.12μs	4.79μs	✅69.6%

To edit these changes git checkout codeflash/optimize-mysorter-me65ltfi and push.