⚡️ Speed up function sorter by 75,821%

[codeflash-ai]

📄 75,821% (758.21x) speedup for sorter in codeflash/bubble_sort.py

⏱️ Runtime : 1.26 seconds → 1.66 milliseconds (best of 450 runs)

📝 Explanation and details

Here is a faster version of your program.
Your original code uses a naive bubble sort with O(n²) complexity and is not optimized.
We'll replace it with Python's built-in sort() (Timsort), which is O(n log n) and modifies the array in place.

All comments are preserved, only relevant modifications are commented.

This is functionally equivalent (identical output/return) but significantly faster for all but the smallest arrays.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 64 Passed
⏪ Replay Tests	✅ 3 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

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

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

# unit tests

# 1. BASIC TEST CASES

def test_sorter_simple_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 3.33μs (63.8% faster)

def test_sorter_simple_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 3.12μs (72.0% faster)

def test_sorter_simple_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 2, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.04μs (57.5% faster)

def test_sorter_duplicates():
    # List with duplicates
    arr = [4, 2, 2, 5, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.21μs (61.0% faster)

def test_sorter_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.92μs (32.8% faster)

def test_sorter_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 3.21μs (24.7% faster)

def test_sorter_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.96μs (26.8% faster)

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.21μs (49.3% faster)

def test_sorter_mixed_signs():
    # List with positive and negative numbers
    arr = [-2, 3, 0, -1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.21μs (48.0% faster)

def test_sorter_floats():
    # List with floats
    arr = [2.5, 3.1, 1.0, 2.0, 3.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.50μs -> 3.88μs (67.7% faster)

def test_sorter_mixed_int_float():
    # List with mixed ints and floats
    arr = [3, 2.5, 1, 4.0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.42μs -> 3.62μs (77.0% faster)

def test_sorter_strings():
    # List of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.38μs (46.9% faster)

def test_sorter_empty_list():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.54μs -> 3.00μs (18.0% faster)

# 2. EDGE TEST CASES

def test_sorter_all_identical():
    # All elements are the same
    arr = [7] * 10
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 3.21μs (89.6% faster)

def test_sorter_large_numbers():
    # List with very large numbers
    arr = [sys.maxsize, -sys.maxsize, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.17μs -> 3.42μs (80.5% faster)

def test_sorter_small_numbers():
    # List with very small numbers
    arr = [sys.maxsize * -1, sys.maxsize * -2, 0, 1, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 3.50μs (51.2% faster)

def test_sorter_already_sorted_large():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 28.8μs (64162% faster)

def test_sorter_reverse_sorted_large():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.1ms -> 29.8μs (104262% faster)

def test_sorter_strings_case_sensitive():
    # List of strings with mixed case
    arr = ["apple", "Banana", "banana", "Apple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 3.33μs (82.5% faster)

def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["café", "cafe", "CAFÉ", "Cafe"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.29μs -> 3.54μs (77.6% faster)

def test_sorter_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.04μs (68.5% faster)


def test_sorter_mixed_types():
    # List with mixed types (int, str) should raise TypeError
    arr = [1, "a", 2, "b"]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_nan_values():
    # List with NaN values: NaN is not equal to itself, but comparisons are allowed in Python
    arr = [float('nan'), 1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.29μs -> 3.21μs (33.8% faster)

def test_sorter_inf_values():
    # List with inf and -inf
    arr = [float('inf'), 1, float('-inf'), 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.25μs (60.3% faster)

# 3. LARGE SCALE TEST CASES

def test_sorter_large_random_ints():
    # Large random list of ints
    arr = random.sample(range(-10000, -9000), 1000)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.3ms -> 69.8μs (40483% faster)

def test_sorter_large_random_floats():
    # Large random list of floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 26.6ms -> 295μs (8882% faster)

def test_sorter_large_random_strings():
    # Large random list of strings
    arr = [
        ''.join(random.choices(string.ascii_letters, k=10))
        for _ in range(1000)
    ]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.0ms -> 114μs (26199% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.8ms -> 60.4μs (40965% faster)

def test_sorter_large_already_sorted():
    # Large list already sorted
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.6ms -> 28.9μs (64256% faster)

def test_sorter_large_reverse_sorted():
    # Large list reverse sorted
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.2ms -> 29.0μs (107213% faster)

def test_sorter_large_all_identical():
    # Large list with all identical elements
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.3ms -> 26.4μs (69338% 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 edge integer values

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

# unit tests

# ---------------- BASIC TEST CASES ----------------

def test_empty_list():
    # Test sorting an empty list should return an empty list
    codeflash_output = sorter([]) # 4.29μs -> 2.83μs (51.4% faster)

def test_single_element():
    # Test sorting a list with a single element should return the same list
    codeflash_output = sorter([42]) # 4.12μs -> 2.83μs (45.6% faster)

def test_sorted_list():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 4.62μs -> 3.00μs (54.2% faster)

def test_reverse_sorted_list():
    # Test sorting a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 5.12μs -> 3.04μs (68.5% faster)

def test_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 2, 5]) # 4.67μs -> 3.00μs (55.6% faster)

def test_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = sorter([2, 3, 2, 1, 3]) # 4.62μs -> 2.96μs (56.3% faster)

def test_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-1, -3, 2, 0, 1]) # 4.54μs -> 3.08μs (47.3% faster)

def test_all_equal():
    # Test sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7]) # 4.38μs -> 3.00μs (45.8% faster)

def test_floats():
    # Test sorting a list with floating point numbers
    codeflash_output = sorter([1.5, 2.3, 0.7, 2.3]) # 6.17μs -> 3.54μs (74.2% faster)

def test_mixed_int_float():
    # Test sorting a list with both integers and floats
    codeflash_output = sorter([3, 1.2, 2, 1.1]) # 5.71μs -> 3.38μs (69.1% faster)

def test_strings():
    # Test sorting a list of strings (lexicographical order)
    codeflash_output = sorter(['banana', 'apple', 'cherry']) # 4.75μs -> 3.12μs (52.0% faster)

def test_single_char_strings():
    # Test sorting a list of single character strings
    codeflash_output = sorter(['c', 'a', 'b']) # 4.33μs -> 3.08μs (40.5% faster)

# ---------------- EDGE TEST CASES ----------------

def test_large_negative_and_positive():
    # Test sorting a list with large negative and positive numbers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 1, -1]
    codeflash_output = sorter(arr[:]) # 5.75μs -> 3.21μs (79.2% faster)

def test_min_max_int():
    # Test sorting a list with min and max integer values
    arr = [sys.maxsize, -sys.maxsize - 1]
    codeflash_output = sorter(arr[:]) # 4.17μs -> 3.04μs (37.0% faster)

def test_already_sorted_with_duplicates():
    # Test sorting an already sorted list with duplicates
    arr = [1, 2, 2, 3, 3, 4, 5]
    codeflash_output = sorter(arr[:]) # 4.83μs -> 2.96μs (63.3% faster)

def test_reverse_sorted_with_duplicates():
    # Test sorting a reverse sorted list with duplicates
    arr = [5, 4, 4, 3, 2, 2, 1]
    codeflash_output = sorter(arr[:]) # 5.79μs -> 3.17μs (82.9% faster)

def test_list_with_none_raises():
    # Test sorting a list containing None should raise TypeError
    with pytest.raises(TypeError):
        sorter([1, None, 2])

def test_list_with_unorderable_types_raises():
    # Test sorting a list with unorderable types should raise TypeError
    with pytest.raises(TypeError):
        sorter([1, "a", 2])

def test_list_with_nan():
    # Test sorting a list with float('nan') - nan should always be at the end in Python's sort
    arr = [3, float('nan'), 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.67μs -> 3.17μs (47.4% faster)

def test_list_with_infinity():
    # Test sorting a list with float('inf') and float('-inf')
    arr = [1, float('inf'), -5, float('-inf')]
    codeflash_output = sorter(arr[:]) # 5.46μs -> 3.25μs (68.0% faster)

def test_list_with_empty_strings():
    # Test sorting a list with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr[:]) # 4.75μs -> 3.00μs (58.3% faster)

def test_list_with_case_sensitive_strings():
    # Test sorting a list with mixed case strings
    arr = ["apple", "Banana", "banana", "Apple"]
    codeflash_output = sorter(arr[:]) # 4.96μs -> 3.04μs (63.0% faster)

def test_list_with_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ["á", "a", "ä", "z"]
    codeflash_output = sorter(arr[:]) # 5.29μs -> 3.33μs (58.7% faster)

def test_list_with_long_strings():
    # Test sorting a list with long strings
    arr = ["x"*100, "a"*100, "m"*100]
    codeflash_output = sorter(arr[:]) # 5.79μs -> 4.00μs (44.8% faster)


def test_mutation_of_input():
    # Test that the input list is mutated in place (bubble sort is in-place)
    arr = [3, 2, 1]
    sorter(arr) # 4.88μs -> 3.50μs (39.3% faster)

# ---------------- 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 = sorter(arr[:]) # 18.6ms -> 29.0μs (64069% faster)

def test_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr[:]) # 31.2ms -> 29.9μs (104269% faster)

def test_large_random_list():
    # Test sorting a large random list
    arr = random.sample(range(1000), 1000)
    codeflash_output = sorter(arr[:]) # 29.1ms -> 70.0μs (41501% faster)

def test_large_list_with_duplicates():
    # Test sorting a large list with many duplicates
    arr = [random.randint(0, 10) for _ in range(1000)]
    codeflash_output = sorter(arr[:]) # 25.7ms -> 69.4μs (36855% faster)

def test_large_list_of_strings():
    # Test sorting a large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    codeflash_output = sorter(arr[:]) # 29.9ms -> 102μs (29185% faster)

def test_large_list_of_negative_numbers():
    # Test sorting a large list of negative numbers
    arr = [random.randint(-10000, -1) for _ in range(1000)]
    codeflash_output = sorter(arr[:]) # 27.5ms -> 77.0μs (35627% faster)

def test_large_list_of_floats():
    # Test sorting a large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    codeflash_output = sorter(arr[:]) # 27.2ms -> 289μs (9299% faster)

def test_large_list_all_equal():
    # Test sorting a large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = sorter(arr[:]) # 18.1ms -> 28.5μs (63265% 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-mcmj243d and push.