⚡️ Speed up function sorter by 145,189%

[codeflash-ai]

📄 145,189% (1,451.89x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.69 seconds → 2.54 milliseconds (best of 120 runs)

📝 Explanation and details

The optimization replaces a manual bubble sort implementation with Python's built-in arr.sort() method.

Key changes:

• Eliminated the nested O(n²) bubble sort loops that perform element-by-element comparisons and swaps
• Replaced with Python's highly optimized Timsort algorithm (O(n log n) worst-case)

Why this leads to massive speedup:

• Bubble sort complexity: The original code performs ~n²/2 comparisons and up to n²/2 swaps for n elements
• Timsort efficiency: Python's built-in sort is implemented in C, uses adaptive algorithms that perform well on partially sorted data, and has much better algorithmic complexity
• Memory access patterns: Built-in sort has better cache locality compared to the random memory access pattern of bubble sort

Test case performance patterns:

• Small lists (< 10 elements): Modest 10-45% improvements due to reduced Python interpreter overhead
• Large lists (1000 elements): Dramatic 10,000-90,000% speedups where algorithmic complexity dominates:
  • Already sorted: 57,607% faster (Timsort's adaptive nature shines)
  • Reverse sorted: 92,409% faster (worst case for bubble sort)
  • Random data: 44,000+ % faster (consistent O(n log n) vs O(n²) difference)

The optimization is most effective for larger datasets where the O(n²) vs O(n log n) complexity difference becomes pronounced.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 60 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	7.60ms	22.9μs	✅33115%
test_bubble_sort.py::test_sort	898ms	156μs	✅574480%
test_bubble_sort_conditional.py::test_sort	11.6μs	7.79μs	✅48.7%
test_bubble_sort_import.py::test_sort	894ms	158μs	✅563807%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	907ms	159μs	✅567088%
test_bubble_sort_parametrized.py::test_sort_parametrized	570ms	156μs	✅364589%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	136μs	49.9μs	✅173%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for testing with large/small numbers

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

# unit tests

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

def test_sorter_empty_list():
    # Test that an empty list returns an empty list
    codeflash_output = sorter([]) # 9.71μs -> 7.62μs (27.3% faster)

def test_sorter_single_element():
    # Test that a single-element list returns itself
    codeflash_output = sorter([42]) # 8.75μs -> 8.75μs (0.000% faster)

def test_sorter_sorted_list():
    # Test that a sorted list remains unchanged
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 10.3μs -> 7.88μs (31.2% faster)

def test_sorter_reverse_sorted_list():
    # Test that a reverse-sorted list is sorted correctly
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 10.8μs -> 8.75μs (23.3% faster)

def test_sorter_unsorted_list():
    # Test that an unsorted list is sorted correctly
    codeflash_output = sorter([3, 1, 4, 1, 5, 9, 2]) # 11.5μs -> 8.79μs (31.3% faster)

def test_sorter_duplicates():
    # Test that duplicate values are handled correctly
    codeflash_output = sorter([2, 3, 2, 1, 3, 1]) # 11.2μs -> 7.75μs (44.1% faster)

def test_sorter_negative_numbers():
    # Test that negative numbers are sorted correctly
    codeflash_output = sorter([-3, -1, -2, 0, 2, 1]) # 10.8μs -> 7.88μs (36.5% faster)

def test_sorter_mixed_positive_negative():
    # Test that a mix of positive and negative numbers is sorted correctly
    codeflash_output = sorter([5, -10, 3, 0, -2, 8]) # 11.1μs -> 8.92μs (24.3% faster)

def test_sorter_already_sorted_with_duplicates():
    # Test that a sorted list with duplicates remains unchanged
    codeflash_output = sorter([1, 2, 2, 3, 3, 4]) # 10.4μs -> 8.75μs (18.6% faster)

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

def test_sorter_all_identical():
    # Test that a list where all elements are identical is unchanged
    codeflash_output = sorter([7, 7, 7, 7, 7]) # 9.96μs -> 8.75μs (13.8% faster)

def test_sorter_two_elements_sorted():
    # Test that two already sorted elements remain unchanged
    codeflash_output = sorter([1, 2]) # 9.71μs -> 7.54μs (28.7% faster)

def test_sorter_two_elements_unsorted():
    # Test that two unsorted elements are swapped
    codeflash_output = sorter([2, 1]) # 9.50μs -> 8.62μs (10.1% faster)

def test_sorter_large_negative_and_positive():
    # Test with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize - 1, 0]
    expected = [-sys.maxsize - 1, 0, sys.maxsize]
    codeflash_output = sorter(arr) # 10.7μs -> 8.17μs (31.1% faster)

def test_sorter_floats():
    # Test with floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0, 4.4]
    expected = [1.0, 2.2, 3.1, 4.4, 5.5]
    codeflash_output = sorter(arr) # 12.8μs -> 8.79μs (45.0% faster)

def test_sorter_mixed_int_float():
    # Test with a mix of ints and floats
    arr = [1, 2.2, 0, 3.3, 2]
    expected = [0, 1, 2, 2.2, 3.3]
    codeflash_output = sorter(arr) # 12.0μs -> 8.17μs (47.5% faster)

def test_sorter_strings():
    # Test with a list of strings
    arr = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(arr) # 10.4μs -> 8.67μs (19.7% faster)

def test_sorter_strings_with_duplicates_and_case():
    # Test with strings, duplicates, and mixed case
    arr = ["Apple", "banana", "apple", "Banana"]
    expected = ["Apple", "Banana", "apple", "banana"]
    codeflash_output = sorter(arr) # 10.7μs -> 8.04μs (32.6% faster)

def test_sorter_unicode_strings():
    # Test with unicode strings
    arr = ["café", "cafe", "cafè"]
    expected = ["cafe", "cafè", "café"]
    codeflash_output = sorter(arr) # 10.9μs -> 9.25μs (18.0% faster)

def test_sorter_empty_strings():
    # Test with empty strings in the list
    arr = ["", "a", "", "b"]
    expected = ["", "", "a", "b"]
    codeflash_output = sorter(arr) # 10.2μs -> 8.88μs (14.5% faster)

def test_sorter_list_with_none_raises():
    # Test that a list with None raises TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr) # 38.9μs -> 37.8μs (2.87% faster)

def test_sorter_list_with_incomparable_types_raises():
    # Test that a list with incomparable types raises TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr) # 39.9μs -> 38.8μs (3.01% faster)

def test_sorter_mutates_input():
    # Test that the input list is mutated (in-place sort)
    arr = [2, 1]
    sorter(arr) # 9.83μs -> 8.67μs (13.5% faster)

def test_sorter_returns_reference_to_input():
    # Test that the returned list is the same object as the input
    arr = [2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 9.62μs -> 8.67μs (11.1% faster)

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

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 30.4ms -> 67.6μs (44819% faster)

def test_sorter_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 20.6ms -> 35.6μs (57607% faster)

def test_sorter_large_reverse_sorted():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 33.2ms -> 36.9μs (89772% faster)

def test_sorter_large_all_identical():
    # Test sorting a large list of all identical elements
    arr = [42] * 1000
    expected = [42] * 1000
    codeflash_output = sorter(arr.copy()) # 19.5ms -> 34.8μs (55929% faster)

def test_sorter_large_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 = sorter(arr.copy()) # 31.3ms -> 96.4μs (32314% faster)

def test_sorter_large_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 28.6ms -> 294μs (9622% faster)

def test_sorter_large_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 = sorter(arr.copy()) # 27.7ms -> 56.9μs (48603% 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 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 # 10.7μs -> 8.29μs (28.6% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 8.71μs (24.9% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.04μs -> 8.62μs (4.83% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 8.04μs (32.1% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.83μs -> 7.83μs (25.5% faster)

def test_sorter_basic_two_elements_sorted():
    # Two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.83μs -> 8.67μs (13.5% faster)

def test_sorter_basic_two_elements_unsorted():
    # Two elements unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.75μs -> 7.83μs (24.5% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.04μs (30.6% faster)

def test_sorter_basic_mixed_signs():
    # List with positive and negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.21μs (31.0% faster)

def test_sorter_basic_floats():
    # List with floats
    arr = [2.2, 1.1, 3.3, 0.0, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.4μs -> 9.58μs (29.1% faster)

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 8.38μs (26.9% faster)

def test_sorter_basic_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.29μs -> 8.12μs (14.4% faster)

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

def test_sorter_edge_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.21μs (23.3% faster)

def test_sorter_edge_large_integers():
    # List with very large integers
    arr = [sys.maxsize, 0, -sys.maxsize - 1, 123456789]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.6μs -> 9.12μs (27.4% faster)

def test_sorter_edge_mixed_types_raises():
    # List with mixed types should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 40.9μs -> 38.8μs (5.37% faster)

def test_sorter_edge_nested_lists_raises():
    # List with nested lists should raise TypeError
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 40.8μs -> 38.8μs (5.05% faster)

def test_sorter_edge_none_in_list_raises():
    # List with None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 40.7μs -> 38.7μs (5.06% faster)

def test_sorter_edge_single_character_strings():
    # List of single-character strings
    arr = list("dcba")
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.96μs (22.8% faster)

def test_sorter_edge_unicode_strings():
    # List with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 9.25μs (31.5% faster)

def test_sorter_edge_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.88μs (21.1% faster)

def test_sorter_edge_floats_and_ints():
    # List with both ints and floats (should sort)
    arr = [1, 2.2, 0, -1.1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 9.04μs (35.5% faster)

def test_sorter_edge_large_negative_numbers():
    # List with large negative numbers
    arr = [-999999999, -1, -1000000000]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.46μs (24.6% faster)

# ---------------- LARGE SCALE TEST CASES ----------------

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.9ms -> 36.6μs (56921% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.8ms -> 36.5μs (92409% faster)

def test_sorter_large_random():
    # Large random list of ints
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.5ms -> 69.2μs (43899% 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 # 27.8ms -> 56.2μs (49399% 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 # 31.4ms -> 97.9μs (32005% 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 # 29.8ms -> 290μs (10159% faster)

def test_sorter_large_all_equal():
    # Large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.9ms -> 32.9μs (60314% faster)

def test_sorter_large_alternating():
    # Large list with alternating values
    arr = [0, 1] * 500
    expected = [0] * 500 + [1] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 23.7ms -> 51.8μs (45589% 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-mdxr09lk and push.