⚡️ Speed up function mysorter by 100,565%

[codeflash-ai]

📄 100,565% (1,005.65x) speedup for mysorter in codeflash/bubble_sort.py

⏱️ Runtime : 2.03 seconds → 2.02 milliseconds (best of 452 runs)

📝 Explanation and details

The optimized code replaces a naive bubble sort implementation with Python's built-in list.sort() method, achieving a 100,564% speedup.

Key optimization:

• Algorithm change: Replaced O(n²) bubble sort with Python's Timsort algorithm (O(n log n))
• Implementation efficiency: Python's list.sort() is implemented in C and highly optimized

Why this leads to massive speedup:

• Asymptotic improvement: Bubble sort requires ~n²/2 comparisons and swaps, while Timsort needs only ~n log n operations
• Native implementation: list.sort() uses optimized C code vs. interpreted Python loops
• Eliminated redundant operations: The original code performed unnecessary passes even when the list was already sorted

Performance characteristics from test results:

• Small lists (5-10 elements): 30-50% faster due to reduced overhead
• Large lists (1000 elements): 30,000-98,000% faster due to algorithmic superiority
• Already sorted data: Timsort's adaptive nature provides exceptional performance (60,000%+ speedup)
• Worst-case scenarios (reverse sorted): Still maintains excellent performance vs. bubble sort's quadratic degradation

The optimization maintains identical behavior including in-place sorting, error handling for incomparable types, and function signature, while dramatically improving performance across all input sizes.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 3 Passed
🌀 Generated Regression Tests	✅ 61 Passed
⏪ Replay Tests	✅ 1 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	1.44s	255μs	✅561890%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # for string sorting tests
import sys  # for maxsize edge cases

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

# unit tests

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

def test_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.38μs -> 3.92μs (11.7% faster)

def test_single_element():
    # Test sorting a list with one element
    arr = [42]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.62μs -> 4.08μs (13.3% faster)

def test_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.04μs -> 4.17μs (45.0% faster)

def test_reverse_sorted_list():
    # Test sorting a reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.38μs -> 4.12μs (54.5% faster)

def test_unsorted_list():
    # Test sorting a typical unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.92μs -> 4.08μs (44.9% faster)

def test_list_with_duplicates():
    # Test sorting a list containing duplicate values
    arr = [2, 3, 2, 1, 4, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.42μs -> 4.21μs (52.5% faster)

def test_list_with_negative_numbers():
    # Test sorting a list containing negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.12μs -> 4.29μs (42.7% faster)

def test_list_with_all_identical_elements():
    # Test sorting a list where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.33μs -> 4.04μs (32.0% faster)

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

def test_list_with_min_and_max_int():
    # Test sorting a list with Python's min and max integer values
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.00μs -> 4.38μs (37.1% faster)

def test_list_with_floats():
    # Test sorting a list with floating point numbers
    arr = [3.14, 2.71, -1.0, 0.0, 2.71]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 8.04μs -> 5.25μs (53.2% faster)

def test_list_with_integers_and_floats():
    # Test sorting a list with both ints and floats
    arr = [1, 2.5, 0, -3.5, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 7.67μs -> 5.04μs (52.0% faster)

def test_list_with_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.58μs -> 4.21μs (32.7% faster)

def test_list_with_mixed_types_raises():
    # Test that sorting a list with mixed incomparable types raises a TypeError
    arr = [1, "apple", 3]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 3.29μs -> 2.58μs (27.4% faster)

def test_list_with_empty_strings():
    # Test sorting a list with empty strings and normal strings
    arr = ["", "a", "abc", ""]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.96μs -> 4.25μs (40.2% faster)

def test_list_with_boolean_values():
    # Test sorting a list with boolean values (should sort as 0/1)
    arr = [True, False, True]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.21μs -> 4.04μs (28.9% faster)

def test_list_with_none_raises():
    # Test that sorting a list with None and ints raises a TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 3.12μs -> 2.50μs (25.0% faster)

def test_list_with_large_and_small_floats():
    # Test sorting a list with very large and very small floats
    arr = [1e308, 1e-308, 0, -1e308]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 10.2μs -> 7.00μs (46.4% faster)

def test_list_with_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ["z", "ä", "a", "ß"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.54μs -> 4.75μs (37.7% faster)

def test_list_with_custom_objects_raises():
    # Test sorting a list with custom objects that do not implement comparison
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 3.12μs -> 2.54μs (23.0% faster)

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

def test_large_sorted_list():
    # Test sorting a large already sorted list (performance and correctness)
    arr = list(range(1000))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 32.0ms -> 52.4μs (61009% faster)

def test_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 51.2ms -> 52.2μs (97850% faster)

def test_large_random_list():
    # Test sorting a large random list of integers
    arr = random.sample(range(1000), 1000)  # 1000 unique ints in random order
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 47.5ms -> 114μs (41561% faster)

def test_large_list_with_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 = mysorter(arr.copy()); result = codeflash_output # 41.6ms -> 91.4μs (45405% faster)

def test_large_string_list():
    # 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()); result = codeflash_output # 50.6ms -> 136μs (36870% faster)

def test_large_list_with_negatives_and_positives():
    # Test sorting a large list with both negative and positive numbers
    arr = [random.randint(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 46.7ms -> 114μs (40652% faster)

# ----------------------
# 4. Additional Robustness Tests
# ----------------------

@pytest.mark.parametrize("arr,expected", [
    ([1, 2, 2, 1, 3, 3, 3], [1, 1, 2, 2, 3, 3, 3]),  # Many duplicates
    ([0], [0]),                                       # Single zero
    ([float('inf'), 1, -float('inf')], [-float('inf'), 1, float('inf')]),  # Infinities
    ([True, 1, False, 0], [False, 0, True, 1]),       # Booleans and ints
    (["apple", "Apple", "banana"], ["Apple", "apple", "banana"]),  # Case sensitivity
])
def test_various_cases(arr, expected):
    # Test various parameterized cases for completeness
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 30.7μs -> 22.1μs (38.8% 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
import string
import sys

# 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
    arr = []
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.62μs -> 4.04μs (39.2% faster)

def test_single_element():
    # Test sorting a list with a single element
    arr = [42]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.29μs -> 4.04μs (30.9% faster)

def test_two_elements_sorted():
    # Test sorting a two-element list that is already sorted
    arr = [1, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.46μs -> 4.00μs (36.4% faster)

def test_two_elements_unsorted():
    # Test sorting a two-element list that is unsorted
    arr = [2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.21μs -> 4.04μs (28.9% faster)

def test_multiple_elements_sorted():
    # Test a list that is already sorted
    arr = [1, 2, 3, 4, 5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.79μs -> 4.17μs (39.0% faster)

def test_multiple_elements_reverse():
    # Test a list sorted in reverse order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.17μs -> 4.25μs (45.1% faster)

def test_multiple_elements_unsorted():
    # Test a list with elements in random order
    arr = [3, 1, 4, 2, 5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.79μs -> 4.17μs (39.0% faster)

def test_duplicates():
    # Test a list with duplicate elements
    arr = [3, 1, 2, 3, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.62μs -> 4.17μs (35.0% faster)

def test_all_equal():
    # Test a list where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.25μs -> 4.04μs (29.9% faster)

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

def test_negative_numbers():
    # Test a list with negative numbers
    arr = [-3, -1, -2, 0, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.62μs -> 4.25μs (32.4% faster)

def test_mixed_positive_negative():
    # Test a list with both negative and positive numbers
    arr = [-10, 0, 5, -2, 3]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.04μs -> 4.25μs (42.1% faster)

def test_large_and_small_numbers():
    # Test a list with very large and very small (negative) numbers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999999, -999999999]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 7.33μs -> 4.79μs (53.1% faster)
    expected = [-sys.maxsize - 1, -999999999, 0, 999999999, sys.maxsize]

def test_floats():
    # Test a list with floating point numbers
    arr = [3.1, 2.4, -1.5, 0.0, 2.4]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 7.96μs -> 5.25μs (51.6% faster)

def test_mixed_int_float():
    # Test a list with both integers and floats
    arr = [1, 2.2, 0, -3.3, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 7.71μs -> 5.04μs (52.9% faster)

def test_strings():
    # Test a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.75μs -> 4.21μs (36.6% faster)

def test_strings_with_duplicates():
    # Test a list of strings with duplicates
    arr = ["dog", "cat", "dog", "ant"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.17μs -> 4.33μs (42.3% faster)

def test_empty_strings():
    # Test a list with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 5.83μs -> 4.17μs (40.0% faster)

def test_unicode_strings():
    # Test a list with unicode characters
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 7.17μs -> 4.75μs (50.9% faster)

def test_list_with_none():
    # Test a list containing None (should raise TypeError)
    arr = [1, None, 3]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 3.42μs -> 2.62μs (30.1% faster)

def test_heterogeneous_types():
    # Test a list with mixed types that cannot be compared (should raise TypeError)
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 3.04μs -> 2.50μs (21.6% faster)

def test_already_sorted_large():
    # Test a large list that is already sorted
    arr = list(range(1000))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 32.0ms -> 52.2μs (61206% faster)

def test_reverse_sorted_large():
    # Test a large list that is reverse sorted
    arr = list(range(999, -1, -1))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 51.1ms -> 52.1μs (98087% faster)

def test_list_with_booleans():
    # Test a list with booleans (True > False)
    arr = [True, False, True, False]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.54μs -> 4.42μs (48.1% faster)

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

def test_large_random_integers():
    # Test sorting a large list of random integers
    arr = random.sample(range(-100000, -99000), 1000)
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 46.4ms -> 118μs (39013% 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()); result = codeflash_output # 44.3ms -> 409μs (10713% faster)

def test_large_random_strings():
    # Test sorting a 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()); result = codeflash_output # 51.2ms -> 142μs (35832% faster)

def test_large_all_duplicates():
    # Test sorting a large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 32.3ms -> 50.1μs (64398% faster)

def test_large_alternating_pattern():
    # Test sorting a large list with an alternating pattern
    arr = [i % 2 for i in range(1000)]  # 0,1,0,1,...
    expected = [0] * 500 + [1] * 500
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 37.0ms -> 71.2μs (51768% faster)

def test_large_already_sorted_floats():
    # Test sorting a large list of already sorted floats
    arr = [float(i) for i in range(1000)]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 31.8ms -> 103μs (30730% faster)

# -------------------------------
# Mutability/Reference Test
# -------------------------------

def test_mutates_input():
    # Test that the input list is mutated (since the implementation is in-place)
    arr = [2, 1, 3]
    arr_copy = arr.copy()
    codeflash_output = mysorter(arr); result = codeflash_output # 6.33μs -> 4.12μs (53.6% faster)

# -------------------------------
# Stability Test
# -------------------------------

def test_stability():
    # Test that sorting is stable for objects with equal keys
    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}')"
    items = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c'), Item(2, 'd')]
    # After stable sort by key: [Item(1, 'a'), Item(1, 'c'), Item(2, 'b'), Item(2, 'd')]
    codeflash_output = mysorter(items.copy()); result = codeflash_output # 8.25μs -> 6.00μs (37.5% 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_bubble_sort.py::test_sort	1.44s	255μs	✅561890%

To edit these changes git checkout codeflash/optimize-mysorter-me60rxfn and push.