⚡️ Speed up function sorter by 109,180%

[codeflash-ai]

📄 109,180% (1,091.80x) speedup for sorter in codeflash/bubble_sort.py

⏱️ Runtime : 2.12 seconds → 1.94 milliseconds (best of 137 runs)

📝 Explanation and details

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

Key changes:

• Eliminated nested loops: The original O(N²) bubble sort algorithm with nested for loops is replaced by a single arr.sort() call
• Leveraged Timsort: Python's built-in sort uses the highly optimized Timsort algorithm with O(N log N) complexity
• Removed manual swapping: The three-line element swapping logic is eliminated entirely

Why this creates massive speedup:

• Algorithmic complexity: Bubble sort performs ~N²/2 comparisons and swaps, while Timsort performs ~N log N operations
• Native implementation: arr.sort() is implemented in C and highly optimized, versus interpreted Python loops
• Adaptive sorting: Timsort performs exceptionally well on partially sorted data, explaining the 50,000%+ speedups on large sorted/reverse-sorted lists

Test case performance patterns:

• Small lists (≤10 elements): 15-46% speedup due to reduced Python interpreter overhead
• Large sorted lists: 50,000%+ speedup as Timsort recognizes existing order
• Large random lists: 40,000%+ speedup from superior algorithmic complexity
• Lists with duplicates: 45,000%+ speedup as Timsort handles duplicates efficiently

The optimization maintains identical functionality (in-place sorting, return value, print statements) while transforming an academic O(N²) algorithm into production-ready O(N log N) performance.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 3 Passed
🌀 Generated Regression Tests	✅ 56 Passed
⏪ Replay Tests	✅ 3 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	885ms	158μs	✅559871%
test_pytest_teststest_bubble_sort_py__replay_test_0.py::test_codeflash_bubble_sort_sorter	878ms	160μs	✅546618%

🌀 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 in edge cases

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

# unit tests

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

def test_sorter_empty_list():
    """Test sorting an empty list."""
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.54μs -> 7.96μs (19.9% faster)

def test_sorter_single_element():
    """Test sorting a list with a single element."""
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.88μs -> 8.42μs (17.3% faster)

def test_sorter_sorted_list():
    """Test sorting an already sorted list."""
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 7.96μs (29.8% faster)

def test_sorter_reverse_sorted_list():
    """Test sorting a reverse-sorted list."""
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.17μs (35.2% faster)

def test_sorter_unsorted_list():
    """Test sorting a basic unsorted list."""
    arr = [3, 1, 4, 1, 5, 9, 2, 6]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 8.42μs (40.1% faster)

def test_sorter_with_duplicates():
    """Test sorting a list with duplicate values."""
    arr = [4, 2, 2, 8, 3, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 8.50μs (33.3% faster)

def test_sorter_negative_numbers():
    """Test sorting a list with negative numbers."""
    arr = [-3, -1, -4, 2, 0, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.58μs (28.2% faster)

def test_sorter_all_equal():
    """Test sorting a list where all elements are equal."""
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 7.88μs (27.5% faster)

def test_sorter_floats():
    """Test sorting a list of floats."""
    arr = [3.1, 2.4, 5.6, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 9.75μs (20.1% faster)

def test_sorter_strings():
    """Test sorting a list of strings."""
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 9.25μs (15.3% faster)

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

def test_sorter_mixed_signs():
    """Test sorting a list with both positive and negative numbers."""
    arr = [0, -10, 5, -3, 8, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 9.25μs (24.3% faster)

def test_sorter_large_and_small_numbers():
    """Test sorting a list with very large and very small numbers."""
    arr = [sys.maxsize, -sys.maxsize, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 9.17μs (32.7% faster)

def test_sorter_min_max_int():
    """Test sorting a list with min and max integer values."""
    arr = [0, -2147483648, 2147483647]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.46μs (24.6% faster)

def test_sorter_unicode_strings():
    """Test sorting a list of unicode strings."""
    arr = ["ápple", "apple", "äpple", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.9μs -> 8.71μs (36.4% faster)

def test_sorter_empty_strings():
    """Test sorting a list containing empty strings."""
    arr = ["", "a", "", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.58μs (24.3% faster)

def test_sorter_large_negative_floats():
    """Test sorting a list with large negative and positive floats."""
    arr = [-1e308, 1e308, 0.0, -1e-308, 1e-308]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 15.0μs -> 11.0μs (35.9% faster)

def test_sorter_all_zeros():
    """Test sorting a list of all zeros."""
    arr = [0, 0, 0, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 8.04μs (28.0% faster)

def test_sorter_already_sorted_with_duplicates():
    """Test sorting a sorted list with duplicates."""
    arr = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.42μs (30.7% faster)

def test_sorter_reverse_sorted_with_duplicates():
    """Test sorting a reverse-sorted list with duplicates."""
    arr = [5, 4, 4, 3, 2, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 8.08μs (46.4% faster)

def test_sorter_single_char_strings():
    """Test sorting a list of single-character strings."""
    arr = list("zxyabc")
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 8.75μs (34.3% faster)

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

def test_sorter_large_sorted_list():
    """Test sorting a large, already sorted list."""
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.8ms -> 36.7μs (53838% faster)

def test_sorter_large_reverse_sorted_list():
    """Test sorting a large, reverse sorted list."""
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.1ms -> 36.7μs (90188% faster)

def test_sorter_large_random_list():
    """Test sorting a large random list of integers."""
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.8ms -> 68.5μs (43446% 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()); result = codeflash_output # 26.5ms -> 56.7μs (46700% 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()); result = codeflash_output # 31.2ms -> 100.0μs (31140% 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()); result = codeflash_output # 27.6ms -> 295μs (9252% 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 to generate large random lists
import sys  # used for testing with sys.maxsize

# imports
import pytest  # used for our unit tests
from codeflash.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 # 11.3μs -> 8.29μs (36.2% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 8.21μs (35.5% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 2, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.08μs (34.0% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 8.17μs (30.1% faster)

def test_sorter_basic_negatives():
    # List with negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.04μs (34.7% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.67μs -> 8.58μs (12.6% faster)

def test_sorter_basic_two_elements():
    # List with two elements
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 8.00μs (25.5% faster)

def test_sorter_basic_all_equal():
    # List where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 7.92μs (27.4% faster)

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.46μs -> 7.96μs (18.9% faster)

def test_sorter_edge_large_integers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.4μs -> 8.54μs (44.9% faster)

def test_sorter_edge_mixed_signs():
    # List with positive, negative, and zero
    arr = [0, -100, 100, -50, 50]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 8.29μs (37.2% faster)

def test_sorter_edge_sorted_with_duplicates():
    # Sorted list with duplicates
    arr = [1, 2, 2, 3, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.6μs -> 8.17μs (41.8% faster)

def test_sorter_edge_floats_and_ints():
    # List with both floats and ints
    arr = [3.5, 2, 4.1, 2.0, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.4μs -> 9.33μs (43.7% faster)

def test_sorter_edge_negative_floats():
    # List with negative floats
    arr = [-1.1, -3.3, -2.2, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 8.75μs (27.1% faster)

def test_sorter_edge_already_sorted_floats():
    # Already sorted floats
    arr = [0.1, 0.2, 0.3, 0.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 8.83μs (29.2% faster)

def test_sorter_edge_all_negative():
    # All negative numbers
    arr = [-5, -4, -3, -2, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.17μs (32.1% faster)

def test_sorter_edge_all_zeros():
    # All zeros
    arr = [0, 0, 0, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 8.33μs (23.5% faster)

def test_sorter_edge_large_gap():
    # List with a large gap between values
    arr = [1, 1000000, -999999, 500, -500]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 8.62μs (36.7% faster)

def test_sorter_edge_alternating_signs():
    # Alternating positive and negative numbers
    arr = [-1, 1, -2, 2, -3, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 8.17μs (36.7% 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 # 19.7ms -> 35.9μs (54727% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.0ms -> 35.8μs (92076% faster)

def test_sorter_large_random():
    # Large random list
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.9ms -> 67.2μs (44371% 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 # 26.5ms -> 56.4μs (46891% faster)

def test_sorter_large_all_same():
    # Large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.2ms -> 33.4μs (57432% faster)

def test_sorter_large_negative_positive():
    # Large list with both negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.5ms -> 73.5μs (40098% faster)

def test_sorter_large_floats():
    # Large list with floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.0ms -> 291μs (9832% faster)

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

def test_sorter_mutation():
    # Ensure that the function mutates the input list (in-place sort)
    arr = [3, 2, 1]
    sorter(arr) # 10.9μs -> 8.33μs (30.5% faster)

def test_sorter_return_value_is_input():
    # Ensure that the returned list is the same object as the input list
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 10.4μs -> 8.08μs (28.9% faster)

def test_sorter_type_error_on_non_comparable():
    # List with non-comparable elements should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr) # 43.7μs -> 42.3μs (3.35% faster)

def test_sorter_type_error_on_mixed_types():
    # List with incompatible types (int and None)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr) # 43.6μs -> 42.4μs (2.85% 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	885ms	158μs	✅559871%
test_pytest_teststest_bubble_sort_py__replay_test_0.py::test_codeflash_bubble_sort_sorter	878ms	160μs	✅546618%

To edit these changes git checkout codeflash/optimize-sorter-mdt7s0l3 and push.