⚡️ Speed up function sorter by 93,340%

[codeflash-ai]

📄 93,340% (933.40x) speedup for sorter in code_to_optimize/bubble_sort_3.py

⏱️ Runtime : 778 milliseconds → 833 microseconds (best of 263 runs)

📝 Explanation and details

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

Key changes:

• Eliminated nested loops and manual element swapping with a single arr.sort() call
• Reduced algorithmic complexity from O(n²) to O(n log n)
• Leverages Python's C-implemented sorting which is significantly faster than Python loops

Why it's faster:
The original bubble sort performs excessive comparisons and swaps. For a 1000-element list, bubble sort makes ~500,000 operations while Timsort makes ~10,000. The built-in sort is also implemented in optimized C code rather than interpreted Python loops.

Performance characteristics:

• Small lists (≤10 elements): 200-400% faster due to reduced overhead
• Large sorted lists: 900,000%+ faster since Timsort detects sorted runs and operates in O(n) time
• Large random/reverse lists: 70,000-1,500,000%+ faster due to superior algorithmic complexity
• Maintains identical behavior: in-place sorting with the same return value

The optimization is universally beneficial across all test cases, with dramatic improvements on larger datasets where the O(n²) vs O(n log n) complexity difference becomes pronounced.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 54 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random
import sys

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort_3 import sorter

# unit tests

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

def test_sorter_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()) # 928ns -> 472ns (96.6% faster)

def test_sorter_single_element():
    # Test sorting a single-element list
    arr = [42]
    codeflash_output = sorter(arr.copy()) # 1.09μs -> 475ns (130% faster)

def test_sorter_two_elements_sorted():
    # Test sorting a two-element list already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()) # 1.53μs -> 525ns (191% faster)

def test_sorter_two_elements_unsorted():
    # Test sorting a two-element list unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()) # 1.70μs -> 536ns (217% faster)

def test_sorter_multiple_elements_sorted():
    # Test sorting a list that's already sorted
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 2.33μs -> 555ns (321% faster)

def test_sorter_multiple_elements_reverse():
    # Test sorting a list in descending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()) # 3.10μs -> 583ns (431% faster)

def test_sorter_multiple_elements_random():
    # Test sorting a random list of positive integers
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()) # 2.81μs -> 596ns (371% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = sorter(arr.copy()) # 3.50μs -> 671ns (422% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()) # 3.14μs -> 667ns (371% faster)

def test_sorter_mixed_sign_numbers():
    # Test sorting a list with both negative and positive numbers
    arr = [0, -1, 3, -2, 2, 1]
    codeflash_output = sorter(arr.copy()) # 3.35μs -> 639ns (425% faster)

def test_sorter_all_identical():
    # Test sorting a list where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()) # 2.06μs -> 526ns (291% faster)

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

def test_sorter_large_negative_and_positive():
    # Test sorting a list with large negative and positive numbers
    arr = [-1000000, 999999, 0, -999999, 1000000]
    codeflash_output = sorter(arr.copy()) # 3.15μs -> 637ns (395% faster)

def test_sorter_floats():
    # Test sorting a list of floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0, 4.4]
    codeflash_output = sorter(arr.copy()) # 2.84μs -> 705ns (303% faster)

def test_sorter_floats_and_integers():
    # Test sorting a list with both floats and integers
    arr = [3, 2.2, 5, 1.0, 4]
    codeflash_output = sorter(arr.copy()) # 3.65μs -> 1.09μs (236% faster)

def test_sorter_extreme_values():
    # Test sorting a list with sys.maxsize and -sys.maxsize-1
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr.copy()) # 2.25μs -> 706ns (219% faster)

def test_sorter_already_sorted_with_duplicates():
    # Test sorting a list that's already sorted but contains duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()) # 3.17μs -> 584ns (443% 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()) # 4.21μs -> 715ns (489% faster)

def test_sorter_with_zeroes():
    # Test sorting a list with zeroes and other numbers
    arr = [0, 0, 1, -1, 2, -2]
    codeflash_output = sorter(arr.copy()) # 3.47μs -> 681ns (410% faster)

def test_sorter_minimal_unsorted():
    # Test sorting a nearly sorted list with only two elements out of place
    arr = [1, 2, 4, 3, 5]
    codeflash_output = sorter(arr.copy()) # 2.57μs -> 624ns (312% faster)

def test_sorter_strings_should_raise():
    # Test sorting a list with non-comparable types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.87μs -> 2.26μs (26.8% faster)

def test_sorter_none_should_raise():
    # Test sorting a list containing None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.53μs -> 2.13μs (19.1% faster)

def test_sorter_nested_lists_should_raise():
    # Test sorting a list containing lists should raise TypeError
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.57μs -> 2.12μs (21.2% faster)

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

def test_sorter_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 44.7ms -> 4.71μs (948404% faster)

def test_sorter_large_reverse():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()) # 69.0ms -> 4.43μs (1557858% faster)

def test_sorter_large_random():
    # Test sorting a large random list
    arr = list(range(1000))
    random.seed(42)
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 60.3ms -> 81.1μs (74271% 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()) # 55.5ms -> 49.6μs (111928% faster)

def test_sorter_large_negative_positive():
    # Test sorting a large list with negative and positive numbers
    arr = [random.randint(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 60.7ms -> 81.1μs (74712% faster)

def test_sorter_large_floats():
    # Test sorting a large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 58.0ms -> 80.4μs (72037% faster)

# -----------------
# ADDITIONAL EDGE CASES FOR ROBUSTNESS
# -----------------

def test_sorter_all_zeroes():
    # Test sorting a list of all zeroes
    arr = [0] * 10
    codeflash_output = sorter(arr.copy()) # 5.54μs -> 544ns (918% faster)

def test_sorter_alternating_high_low():
    # Test sorting a list with alternating high and low values
    arr = [1, 1000, 2, 999, 3, 998]
    codeflash_output = sorter(arr.copy()) # 3.75μs -> 679ns (453% faster)

def test_sorter_stability():
    # Test that sorting is stable for equal elements (should preserve order)
    # Bubble sort is stable, so this should pass
    class Item:
        def __init__(self, key, value):
            self.key = key
            self.value = value
        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.value == other.value
        def __repr__(self):
            return f"Item({self.key}, {self.value})"
    arr = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c'), Item(2, 'd')]
    codeflash_output = sorter(arr.copy()); sorted_arr = codeflash_output # 3.98μs -> 1.70μs (135% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort_3 import sorter

# unit tests

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

def test_sorter_basic_sorted():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 2.28μs -> 553ns (312% faster)

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted in ascending order
    arr = [5, 3, 1, 4, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 2.84μs -> 637ns (346% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.01μs -> 558ns (439% faster)

def test_sorter_basic_duplicates():
    # List with duplicate values should be sorted and duplicates preserved
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 2.80μs -> 598ns (368% faster)

def test_sorter_basic_single_element():
    # List with a single element should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 982ns -> 457ns (115% faster)

def test_sorter_basic_two_elements():
    # List with two elements should be sorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 1.57μs -> 530ns (197% faster)

def test_sorter_basic_all_equal():
    # List where all elements are equal should remain unchanged
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 1.90μs -> 548ns (247% faster)

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

def test_sorter_edge_empty_list():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 771ns -> 468ns (64.7% faster)

def test_sorter_edge_negative_numbers():
    # List with negative numbers should be sorted correctly
    arr = [-3, -1, -2, -5, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 2.91μs -> 648ns (349% faster)

def test_sorter_edge_mixed_signs():
    # List with both positive and negative numbers
    arr = [0, -1, 2, -3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 2.79μs -> 647ns (331% faster)

def test_sorter_edge_floats():
    # List with floating point numbers
    arr = [3.2, 1.5, 2.8, 1.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 2.45μs -> 661ns (270% faster)

def test_sorter_edge_mixed_int_float():
    # List with both ints and floats
    arr = [3, 2.5, 1, 4.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.10μs -> 937ns (231% faster)

def test_sorter_edge_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [1e10, -1e10, 0, 1e-10, -1e-10]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 1.15μs (238% faster)

def test_sorter_edge_already_sorted_with_duplicates():
    # Already sorted list with duplicates
    arr = [1, 2, 2, 3, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.14μs -> 545ns (476% faster)

def test_sorter_edge_min_max_int():
    # List with min and max integer values
    import sys
    arr = [sys.maxsize, -sys.maxsize - 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 2.27μs -> 695ns (227% faster)

def test_sorter_edge_mutation_protection():
    # Ensure the original list is not mutated (if function is expected to be pure)
    arr = [3, 1, 2]
    arr_copy = arr.copy()
    sorter(arr_copy) # 2.03μs -> 570ns (256% faster)

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

def test_sorter_large_sorted():
    # Large sorted list should remain unchanged
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 43.9ms -> 4.87μs (901275% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list should be sorted
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 69.3ms -> 4.55μs (1524151% faster)

def test_sorter_large_random():
    # Large random list should be sorted
    import random
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 61.9ms -> 84.4μs (73262% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [2] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 56.8ms -> 5.75μs (987914% faster)

def test_sorter_large_negative_positive():
    # Large list with negative and positive numbers
    arr = list(range(-500, 500))
    random_arr = arr.copy()
    import random
    random.shuffle(random_arr)
    codeflash_output = sorter(random_arr.copy()); result = codeflash_output # 60.4ms -> 86.9μs (69419% faster)

def test_sorter_large_floats():
    # Large list with floats
    arr = [float(i) / 10 for i in range(1000)]
    import random
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 58.2ms -> 82.7μs (70235% faster)

def test_sorter_large_mixed_types():
    # Large list with ints and floats
    arr = [i if i % 2 == 0 else float(i) for i in range(1000)]
    import random
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 79.3ms -> 230μs (34277% 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-meo8q5nx and push.