⚡️ Speed up function sorter by 197,541%

[codeflash-ai]

📄 197,541% (1,975.41x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 5.92 seconds → 2.99 milliseconds (best of 442 runs)

📝 Explanation and details

The optimization replaces the O(N²) bubble sort implementation with Python's built-in arr.sort(), which uses the highly optimized Timsort algorithm (O(N log N)).

Key Changes:

• Removed the nested loop structure that performed 83+ million operations for large inputs
• Replaced manual element swapping with Python's native sorting implementation
• Eliminated redundant len(arr) - 1 calculations in the inner loop

Why This Creates Massive Speedup:
The original bubble sort has quadratic time complexity, making ~N²/2 comparisons and up to N²/2 swaps. Python's Timsort is a hybrid stable sorting algorithm that:

• Runs in O(N log N) worst case, O(N) best case for already-sorted data
• Uses highly optimized C implementation
• Employs intelligent techniques like run detection and galloping mode

Performance by Test Case Type:

• Small lists (≤10 elements): 20-60% faster - overhead reduction from eliminating nested loops
• Large sorted lists: 60,000%+ faster - Timsort detects existing order and runs in near-linear time
• Large random/reverse sorted lists: 40,000-98,000%+ faster - demonstrates the O(N log N) vs O(N²) algorithmic advantage
• Lists with duplicates: 37,000-84,000%+ faster - Timsort handles duplicates efficiently without unnecessary comparisons

The line profiler shows the original code spent 26.5% of time just in the inner loop range calculation and 30.6% in comparisons, totaling over 83 million operations. The optimized version eliminates this entirely with a single efficient sort call.

✅ 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	11.3ms	28.4μs	✅39574%
test_bubble_sort.py::test_sort	1.44s	252μs	✅570982%
test_bubble_sort_conditional.py::test_sort	7.92μs	4.25μs	✅86.3%
test_bubble_sort_import.py::test_sort	1.43s	253μs	✅563823%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	1.43s	254μs	✅562505%
test_bubble_sort_parametrized.py::test_sort_parametrized	894ms	254μs	✅351754%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	144μs	30.1μs	✅381%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random test cases
import string  # used for string sorting test cases

# 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 # 6.50μs -> 4.46μs (45.8% faster)

def test_sorter_basic_reverse_sorted():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.42μs -> 4.25μs (51.0% faster)

def test_sorter_basic_unsorted():
    # Unsorted list with mixed values
    arr = [3, 1, 4, 2, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.17μs -> 4.29μs (43.7% faster)

def test_sorter_basic_duplicates():
    # List with duplicate values
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 4.21μs (44.6% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 4.12μs (20.2% faster)

def test_sorter_basic_two_elements():
    # List with two elements
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 4.12μs (21.2% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, -5, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.42μs -> 4.46μs (43.9% faster)

def test_sorter_basic_mixed_sign_numbers():
    # List with positive and negative numbers
    arr = [3, -1, 2, -5, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 4.25μs (44.1% faster)

def test_sorter_basic_floats():
    # List with floats
    arr = [1.2, 3.4, 2.2, 0.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.75μs -> 5.12μs (51.2% faster)

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.29μs -> 4.50μs (39.8% faster)

def test_sorter_basic_empty_list():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 3.88μs (9.68% faster)

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

def test_sorter_edge_all_identical():
    # All elements are the same
    arr = [7] * 10
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.71μs -> 4.42μs (74.5% faster)

def test_sorter_edge_alternating_values():
    # Alternating high/low values
    arr = [1, 1000] * 5
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.21μs -> 4.88μs (88.9% faster)

def test_sorter_edge_large_numbers():
    # Very large numbers
    arr = [10**10, -10**10, 0, 9999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.42μs -> 4.62μs (38.7% faster)

def test_sorter_edge_small_numbers():
    # Very small numbers
    arr = [1e-10, -1e-10, 0, 1e-9]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.96μs -> 6.08μs (30.8% faster)

def test_sorter_edge_mixed_types_int_float():
    # Mixed ints and floats
    arr = [3, 2.5, 1, 4.0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.79μs -> 5.00μs (55.8% faster)

def test_sorter_edge_strings_with_case():
    # Strings with different cases
    arr = ["banana", "Apple", "cherry", "Date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 4.46μs (34.6% faster)

def test_sorter_edge_unicode_strings():
    # Unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.96μs -> 4.83μs (44.0% faster)

def test_sorter_edge_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 4.33μs (38.5% faster)

def test_sorter_edge_none_in_list():
    # List with None (should raise TypeError)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.54μs -> 2.75μs (28.8% faster)

def test_sorter_edge_incompatible_types():
    # List with incompatible types (should raise TypeError)
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.08μs -> 2.58μs (19.4% faster)

def test_sorter_edge_mutation():
    # Ensure the original list is mutated (in-place sort)
    arr = [3, 2, 1]
    sorter(arr) # 5.42μs -> 4.17μs (30.0% faster)

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

def test_sorter_large_sorted():
    # Large sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.2ms -> 52.2μs (61497% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.4ms -> 52.2μs (98235% faster)

def test_sorter_large_random_integers():
    # Large random integer list
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 47.0ms -> 115μs (40525% faster)

def test_sorter_large_random_floats():
    # Large random float list
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 45.1ms -> 405μs (11016% 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 # 50.8ms -> 135μs (37264% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [3] * 500
    expected = [3] * 500 + [5] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 42.1ms -> 50.2μs (83770% faster)

def test_sorter_large_alternating():
    # Large list alternating between two values
    arr = [0, 1] * 500
    expected = [0]*500 + [1]*500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 37.2ms -> 71.0μs (52250% faster)

def test_sorter_large_edge_min_max():
    # Large list with min/max at ends
    arr = [1000] + list(range(1, 999)) + [0]
    expected = [0] + list(range(1, 999)) + [1000]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.4ms -> 54.1μs (59746% faster)

# -------------------- DETERMINISM TEST --------------------

def test_sorter_determinism():
    # Sorting the same list twice should give the same result
    arr = [5, 3, 1, 4, 2]
    codeflash_output = sorter(arr.copy()); result1 = codeflash_output # 6.79μs -> 4.42μs (53.8% faster)
    codeflash_output = sorter(arr.copy()); result2 = codeflash_output # 5.42μs -> 4.00μs (35.4% faster)

# -------------------- STABILITY TEST --------------------

def test_sorter_stability():
    # Bubble sort is stable; test with tuples (value, original_index)
    arr = [(2, 'a'), (1, 'b'), (2, 'c'), (1, 'd')]
    # Sort by first element only
    def cmp(x): return x[0]
    expected = sorted(arr, key=cmp)
    # Patch sorter to use a key function for stability test
    arr_copy = arr.copy()
    for i in range(len(arr_copy)):
        for j in range(len(arr_copy) - 1):
            if arr_copy[j][0] > arr_copy[j + 1][0]:
                arr_copy[j], arr_copy[j + 1] = arr_copy[j + 1], arr_copy[j]
# 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 scale test data

# 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 should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.88μs -> 4.33μs (58.6% 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 # 6.42μs -> 4.21μs (52.5% faster)

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted correctly
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.92μs -> 4.25μs (39.2% faster)

def test_sorter_basic_duplicates():
    # List with duplicate elements should be sorted, duplicates preserved
    arr = [2, 3, 2, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 4.21μs (45.6% faster)

def test_sorter_basic_single():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 4.12μs (18.2% faster)

def test_sorter_basic_empty():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.88μs (15.0% faster)

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

def test_sorter_edge_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.54μs -> 4.04μs (37.1% faster)

def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -7, -5, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.21μs -> 4.38μs (41.9% faster)

def test_sorter_edge_mixed_sign_numbers():
    # List with both positive and negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 4.33μs (40.4% faster)

def test_sorter_edge_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [999999999, -999999999, 0, 123456789, -123456789]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.21μs -> 4.75μs (51.7% faster)

def test_sorter_edge_floats_and_integers():
    # List with floats and integers
    arr = [3.5, 2, 4.1, 3, 2.7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.50μs -> 5.29μs (60.6% faster)

def test_sorter_edge_floats_only():
    # List with only floats
    arr = [2.2, 1.1, 3.3, 0.0, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.50μs -> 5.12μs (46.3% faster)

def test_sorter_edge_empty_list():
    # Edge case: empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.21μs -> 3.83μs (9.78% faster)

def test_sorter_edge_two_elements_sorted():
    # Edge case: two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 4.17μs (15.0% faster)

def test_sorter_edge_two_elements_unsorted():
    # Edge case: two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 4.00μs (20.8% faster)

def test_sorter_edge_large_range():
    # Edge case: large range of numbers, including zero
    arr = [0, -1000, 1000, -500, 500]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.54μs -> 4.42μs (48.1% faster)

def test_sorter_edge_stability():
    # Edge case: check stability for equal elements (should preserve order)
    arr = [(2, 'a'), (1, 'b'), (2, 'c'), (1, 'd')]
    # Sorting by first element only, so we need a custom key
    # Our sorter doesn't support keys, so we test with numbers only for stability
    arr = [2, 1, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 4.17μs (34.0% faster)

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

def test_sorter_large_sorted():
    # Large sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.3ms -> 52.1μs (61872% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.3ms -> 52.1μs (98332% faster)

def test_sorter_large_random():
    # Large random list
    arr = list(range(1000))
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 45.3ms -> 116μs (38888% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [3] * 500
    expected = [3] * 500 + [5] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 42.1ms -> 50.0μs (84232% faster)

def test_sorter_large_negative_and_positive():
    # Large list with negative and positive numbers
    arr = list(range(-500, 500))
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 45.3ms -> 114μs (39563% faster)

def test_sorter_large_floats():
    # Large list with floats
    arr = [float(i) / 10 for i in range(1000)]
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 44.7ms -> 222μs (20048% faster)

def test_sorter_large_edge_values():
    # Large list with edge integer values
    arr = [-(2**31), 0, 2**31-1] * 333 + [0]
    expected = [-(2**31)] * 333 + [0] * 334 + [2**31-1] * 333
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 43.0ms -> 90.7μs (47336% faster)

def test_sorter_large_already_sorted_with_duplicates():
    # Large list, already sorted, with duplicates
    arr = [1] * 500 + [2] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.9ms -> 49.1μs (64774% faster)

def test_sorter_large_single_element():
    # Large list with single repeated element
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.9ms -> 50.2μs (63435% faster)

def test_sorter_large_empty():
    # Large scale edge: empty list (should not fail)
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 4.08μs (24.5% 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-me60jcns and push.