⚡️ Speed up function sorter by 195,917%

[codeflash-ai]

📄 195,917% (1,959.17x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 9.96 seconds → 5.08 milliseconds (best of 142 runs)

📝 Explanation and details

The optimization replaces the manual bubble sort implementation with Python's built-in list.sort() method, resulting in a 195,916% speedup.

Key Changes:

• Algorithm replacement: The nested loops performing O(n²) bubble sort comparisons and swaps are replaced with a single arr.sort() call
• Timsort advantage: Python's built-in sort uses Timsort, a highly optimized hybrid sorting algorithm implemented in C that runs in O(n log n) time with excellent performance on real-world data patterns

Why This Creates Massive Speedup:

• Complexity reduction: From O(n²) bubble sort to O(n log n) Timsort
• C implementation: Built-in sort is implemented in optimized C code rather than interpreted Python
• Elimination of Python overhead: No more 32+ million Python bytecode operations for comparisons, assignments, and array accesses visible in the line profiler

Test Case Performance Patterns:

• Small lists (≤10 elements): 13-61% speedup due to reduced Python overhead
• Large lists (1000 elements): 19,000-107,000% speedup where the O(n²) vs O(n log n) complexity difference dominates
• Already sorted lists: Timsort's adaptive nature provides exceptional performance on pre-sorted data
• Lists with duplicates: Timsort handles duplicate-heavy datasets very efficiently

The optimization maintains identical behavior (in-place sorting, same return value) while leveraging Python's most optimized sorting implementation.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 66 Passed
⏪ Replay Tests	✅ 5 Passed
🔎 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	18.4ms	43.8μs	41924%✅
test_bubble_sort.py::test_sort	2.40s	408μs	585710%✅
test_bubble_sort_conditional.py::test_sort	11.0μs	5.97μs	84.2%✅
test_bubble_sort_import.py::test_sort	2.40s	409μs	585123%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	2.42s	410μs	589138%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	1.53s	411μs	372149%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	235μs	38.4μs	513%✅

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for edge cases with strings

# 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 # 9.01μs -> 5.78μs (55.8% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.28μs -> 5.75μs (61.5% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.70μs -> 5.88μs (48.0% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.97μs -> 5.88μs (52.6% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.73μs -> 5.59μs (20.4% faster)

def test_sorter_basic_two_elements():
    # Two element list
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.07μs -> 5.59μs (26.6% faster)

def test_sorter_basic_all_equal():
    # All elements equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.72μs -> 5.55μs (39.0% faster)

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.69μs -> 5.00μs (13.8% faster)

def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, -5, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.52μs -> 6.20μs (53.6% faster)

def test_sorter_edge_mixed_sign_numbers():
    # List with positive and negative numbers
    arr = [0, -1, 2, -3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.16μs -> 6.03μs (52.0% faster)

def test_sorter_edge_floats():
    # List with floats
    arr = [3.5, 2.1, 4.0, 3.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 8.04μs (35.5% faster)

def test_sorter_edge_floats_and_ints():
    # List with ints and floats
    arr = [1, 2.2, 3, 0.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 8.23μs (43.7% 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 # 18.0μs -> 11.8μs (52.1% faster)

def test_sorter_edge_strings():
    # List with strings (should sort lexicographically)
    arr = ["banana", "apple", "pear", "apple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.61μs -> 6.55μs (46.8% faster)

def test_sorter_edge_unicode_strings():
    # List with unicode strings
    arr = ["ápple", "apple", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 7.33μs (39.0% faster)

def test_sorter_edge_mixed_types_raises():
    # List with mixed types should raise TypeError
    arr = [1, "apple", 2.2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 5.83μs -> 4.94μs (18.1% faster)

def test_sorter_edge_none_in_list_raises():
    # List with None and numbers should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 5.86μs -> 4.78μs (22.5% faster)

def test_sorter_edge_tuple_in_list_raises():
    # List with tuple and numbers should raise TypeError
    arr = [(1, 2), 3, 4]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 5.87μs -> 4.87μs (20.5% faster)

def test_sorter_edge_boolean_values():
    # List with boolean values (since bool is a subclass of int)
    arr = [True, False, 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.40μs -> 6.57μs (43.1% faster)

def test_sorter_edge_long_strings():
    # List with long strings
    arr = ["a"*100, "b"*99, "a"*101]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.22μs (33.8% faster)

def test_sorter_edge_special_characters():
    # List with special characters
    arr = ["!", "@", "#", "$", "%"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 6.60μs (59.6% 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 # 55.7ms -> 79.4μs (70040% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 85.5ms -> 79.4μs (107502% faster)

def test_sorter_large_random():
    # Large random list
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 76.5ms -> 179μs (42460% 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 # 69.8ms -> 131μs (53126% faster)

def test_sorter_large_strings():
    # Large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=10)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 84.9ms -> 283μs (29822% faster)

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-1e5, 1e5) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 71.0ms -> 763μs (9210% faster)

def test_sorter_large_already_equal():
    # Large list where all elements are equal
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 55.7ms -> 76.8μs (72442% faster)

def test_sorter_large_edge_min_max():
    # Large list with only min and max values
    arr = [float('-inf'), float('inf')] * 500
    expected = [float('-inf')] * 500 + [float('inf')] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 61.0ms -> 120μs (50562% faster)

# ----------- MUTATION TESTING DEFENSE -----------

@pytest.mark.parametrize("mutated_arr, expected", [
    ([5, 4, 3, 2, 1], [1, 2, 3, 4, 5]),  # classic reverse
    ([1, 2, 3, 4, 5], [1, 2, 3, 4, 5]),  # already sorted
    ([2, 1, 2, 1], [1, 1, 2, 2]),        # duplicates
    (["b", "a", "c"], ["a", "b", "c"]),  # strings
])
def test_sorter_mutation_defense(mutated_arr, expected):
    # Ensures that the function sorts correctly and is mutation-resistant
    codeflash_output = sorter(mutated_arr.copy()); result = codeflash_output # 35.9μs -> 23.9μs (50.4% 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 import sorter

# unit tests

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

def test_basic_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.83μs -> 5.71μs (54.6% faster)

def test_basic_reverse_sorted_list():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.23μs -> 5.75μs (60.7% faster)

def test_basic_unsorted_list():
    # Unsorted list with random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.84μs -> 5.88μs (50.3% faster)

def test_basic_duplicates():
    # List with duplicate values
    arr = [2, 3, 1, 2, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.36μs -> 5.83μs (43.4% faster)

def test_basic_single_element():
    # Single-element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.50μs -> 5.57μs (16.6% faster)

def test_basic_two_elements_sorted():
    # Two-element sorted list
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.12μs -> 5.64μs (26.2% faster)

def test_basic_two_elements_unsorted():
    # Two-element unsorted list
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.27μs -> 5.66μs (28.6% faster)

def test_basic_empty_list():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.61μs -> 4.97μs (12.8% faster)

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

def test_edge_all_identical():
    # All elements are identical
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.29μs -> 5.68μs (45.9% faster)

def test_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, -5, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.17μs -> 6.21μs (47.6% faster)

def test_edge_mixed_sign_numbers():
    # List with both positive and negative numbers
    arr = [3, -1, 0, -2, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.91μs -> 6.34μs (56.3% faster)

def test_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 # 10.5μs -> 6.59μs (59.3% faster)

def test_edge_floats():
    # List with floating point numbers
    arr = [3.2, 1.5, 2.8, 0.0, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.6μs -> 8.57μs (46.4% faster)

def test_edge_mixed_int_float():
    # List with both ints and floats
    arr = [2, 1.1, 1, 2.2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 7.95μs (52.1% faster)

def test_edge_large_gap():
    # List with large gaps between numbers
    arr = [1, 1000, 10, 100, 10000]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.68μs -> 6.49μs (49.2% faster)

def test_edge_already_sorted_descending():
    # Already sorted descending should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.09μs -> 5.88μs (54.7% faster)

def test_edge_empty_list():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.74μs -> 5.05μs (13.6% faster)

def test_edge_list_with_zeroes():
    # List with zeroes and other numbers
    arr = [0, 0, 1, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.44μs -> 6.11μs (54.5% faster)

def test_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 # 9.23μs -> 6.73μs (37.1% faster)

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

def test_large_already_sorted():
    # Large sorted list should remain unchanged
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 55.7ms -> 78.7μs (70691% faster)

def test_large_reverse_sorted():
    # Large reverse sorted list should be sorted ascending
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 85.5ms -> 79.5μs (107468% faster)

def test_large_random():
    # Large random list should be sorted ascending
    import random
    arr = list(range(1000))
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 75.9ms -> 182μs (41451% faster)

def test_large_all_identical():
    # Large list with all identical values
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 56.1ms -> 77.0μs (72824% faster)

def test_large_many_duplicates():
    # Large list with many duplicates
    arr = [i % 10 for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 71.2ms -> 125μs (56384% faster)

def test_large_negative_and_positive():
    # Large list with negative and positive numbers
    arr = [i if i % 2 == 0 else -i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 70.9ms -> 105μs (67238% faster)

def test_large_floats():
    # Large list with floats
    arr = [float(i) / 10 for i in range(1000)]
    random_arr = arr.copy()
    import random
    random.shuffle(random_arr)
    codeflash_output = sorter(random_arr.copy()); result = codeflash_output # 71.5ms -> 358μs (19859% faster)

def test_large_edge_min_max_int():
    # Large list with min and max int values at random positions
    import sys
    arr = list(range(998))
    arr.insert(0, sys.maxsize)
    arr.insert(0, -sys.maxsize - 1)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 55.7ms -> 90.3μs (61583% faster)

# -------------------------------
# 4. Additional Edge Cases
# -------------------------------

def test_edge_mutation():
    # Ensure that the input list is mutated in place (since the function sorts in place)
    arr = [3, 2, 1]
    sorter(arr) # 9.08μs -> 5.67μs (60.1% faster)

def test_edge_non_integer_types():
    # List with booleans (since bool is a subclass of int)
    arr = [True, False, 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.41μs -> 6.71μs (40.2% faster)

def test_edge_large_gap_floats():
    # List with very large and very small floats
    arr = [1e-10, 1e10, 1.0, -1e10, -1e-10]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 17.9μs -> 12.1μs (48.4% faster)

def test_edge_sorter_returns_reference():
    # Ensure that sorter returns the same reference as input
    arr = [2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 7.57μs -> 5.81μs (30.2% faster)

# -------------------------------
# 5. Error Cases (Type Safety)
# -------------------------------

def test_error_non_comparable():
    # List with non-comparable types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 6.46μs -> 5.00μs (29.2% faster)

def test_error_none_in_list():
    # List with None and int should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 6.05μs -> 4.91μs (23.1% 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_pytest_code_to_optimizetestspytest__replay_test_0.py::test_code_to_optimize_bubble_sort_sorter	93.9ms	225μs	41617%✅

To edit these changes git checkout codeflash/optimize-sorter-mf3iomte and push.