⚡️ Speed up function sorter by 145,096%

[codeflash-ai]

📄 145,096% (1,450.96x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.66 seconds → 2.52 milliseconds (best of 102 runs)

📝 Explanation and details

Here is an optimized version of your code.
The original code uses an inefficient O(n²) bubble sort with unnecessary traversals.
Below, I replaced it with Python's built-in sort (which is Timsort, worst-case O(n log n)), preserving all original print statements and function signature.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 58 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	7.54ms	22.5μs	✅33356%
test_bubble_sort.py::test_sort	898ms	157μs	✅571281%
test_bubble_sort_conditional.py::test_sort	12.4μs	8.58μs	✅44.7%
test_bubble_sort_import.py::test_sort	899ms	153μs	✅586384%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	896ms	157μs	✅569301%
test_bubble_sort_parametrized.py::test_sort_parametrized	562ms	155μs	✅361427%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	136μs	52.5μs	✅160%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random test cases
import string  # used for string sorting tests
import sys  # used for edge case with large/small numbers

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

# unit tests

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

def test_sorter_empty_list():
    # Test that an empty list returns an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.21μs -> 9.08μs (1.38% faster)

def test_sorter_single_element():
    # Test that a single-element list returns the same single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.33μs -> 8.33μs (12.0% faster)

def test_sorter_already_sorted():
    # Test that an already sorted list is unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.62μs -> 9.21μs (4.53% faster)

def test_sorter_reverse_sorted():
    # Test that a reverse-sorted list is sorted correctly
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.67μs -> 8.50μs (13.7% faster)

def test_sorter_unsorted_integers():
    # Test a typical unsorted list of integers
    arr = [3, 1, 4, 1, 5, 9, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 8.54μs (32.2% faster)

def test_sorter_duplicates():
    # Test a list with duplicate values
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 9.17μs (14.6% faster)

def test_sorter_negative_numbers():
    # Test a list with negative numbers
    arr = [-3, -1, -2, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.79μs (19.9% faster)

def test_sorter_floats():
    # Test a list of floats
    arr = [3.1, 2.4, 5.6, 1.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.3μs -> 9.38μs (31.6% faster)

def test_sorter_mixed_int_float():
    # Test a list with both ints and floats
    arr = [1, 2.2, 3, 0.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 10.0μs (20.3% faster)

def test_sorter_strings():
    # Test a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.62μs (25.1% faster)

def test_sorter_mixed_case_strings():
    # Test a list of strings with mixed case
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.38μs (21.9% faster)

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

def test_sorter_all_equal():
    # Test a list where all elements are equal
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.54μs (25.4% faster)

def test_sorter_min_max_int():
    # Test a list with minimum and maximum integer values
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 1, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 9.08μs (30.3% faster)

def test_sorter_min_max_float():
    # Test a list with minimum and maximum float values
    arr = [float('inf'), float('-inf'), 0.0, 1.1, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 10.0μs (21.2% faster)

def test_sorter_special_floats():
    # Test a list with NaN, inf, -inf (NaN is always unordered)
    arr = [float('nan'), float('inf'), 1.0, float('-inf'), 0.0]
    # Since NaN is not equal to anything, including itself, we check that all other elements are sorted and NaN is present
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 8.96μs (26.1% faster)
    non_nan = [x for x in result if not (isinstance(x, float) and str(x) == 'nan')]

def test_sorter_empty_strings():
    # Test a list of empty strings and normal strings
    arr = ["", "a", "", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 9.38μs (16.4% faster)

def test_sorter_unicode_strings():
    # Test a list of unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.0μs -> 8.75μs (37.1% faster)

def test_sorter_large_negative_positive():
    # Test a list with very large and very small numbers
    arr = [1e308, -1e308, 0, 1e-308, -1e-308]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 16.2μs -> 12.4μs (30.9% faster)

def test_sorter_mutable_input():
    # Test that the original list is mutated (since the implementation is in-place)
    arr = [3, 2, 1]
    sorter(arr) # 10.2μs -> 8.33μs (23.0% faster)

def test_sorter_non_comparable_types():
    # Test that a list with non-comparable types raises TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.7μs -> 42.5μs (2.85% faster)


def test_sorter_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.0ms -> 36.5μs (54700% faster)

def test_sorter_large_reverse_sorted():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.3ms -> 37.2μs (89433% faster)

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.6ms -> 75.5μs (40345% 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)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.3ms -> 63.4μs (42898% 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)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.8ms -> 110μs (28724% faster)

def test_sorter_large_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.1ms -> 299μs (8931% 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 maxsize edge 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_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 8.38μs (32.3% faster)

def test_sorter_reverse_sorted_list():
    # Reversed list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.29μs (33.2% faster)

def test_sorter_unsorted_list():
    # Unsorted list with random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.33μs (28.5% faster)

def test_sorter_with_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 8.17μs (36.7% faster)

def test_sorter_single_element():
    # Single element should return unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.62μs -> 8.42μs (14.4% faster)

def test_sorter_two_elements_sorted():
    # Two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.67μs -> 8.25μs (17.2% faster)

def test_sorter_two_elements_unsorted():
    # Two elements unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.88μs -> 7.92μs (24.7% faster)

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.54μs (24.9% faster)

def test_sorter_mixed_positive_negative():
    # List with both positive and negative numbers
    arr = [5, -1, 3, 0, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.33μs (28.0% faster)

def test_sorter_all_same():
    # All elements the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.08μs (25.8% faster)

def test_sorter_floats():
    # List of floats
    arr = [3.1, 2.2, 5.5, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.3μs -> 9.50μs (29.8% faster)

def test_sorter_ints_and_floats():
    # Mixed ints and floats
    arr = [1, 2.2, 3, 0.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 9.25μs (31.1% faster)

def test_sorter_strings():
    # List of strings
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.83μs (21.2% faster)

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.29μs -> 8.29μs (12.1% faster)

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

def test_sorter_large_negative_and_positive():
    # 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 # 12.3μs -> 9.17μs (34.5% faster)

def test_sorter_min_max_float():
    # List with min and max float values
    arr = [float('inf'), float('-inf'), 0.0, 1.5, -1.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 8.75μs (38.6% faster)

def test_sorter_nan_values():
    # List with NaN values (should sort, but NaN always compares False)
    arr = [float('nan'), 1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.12μs (26.2% faster)

def test_sorter_strings_case_sensitive():
    # Case sensitivity in strings
    arr = ['banana', 'Apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.00μs (26.6% faster)

def test_sorter_unicode_strings():
    # Unicode strings
    arr = ['ápple', 'apple', 'äpple']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 9.75μs (14.5% faster)

def test_sorter_mutation_side_effect():
    # Ensure the input list is mutated (since the function sorts in-place)
    arr = [3, 2, 1]
    sorter(arr) # 10.3μs -> 7.96μs (29.3% faster)

def test_sorter_empty_string_elements():
    # List with empty strings
    arr = ['', 'a', '']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 8.08μs (27.8% faster)

def test_sorter_boolean_values():
    # List with boolean values (False < True)
    arr = [True, False, True, False]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.62μs -> 8.71μs (10.5% faster)

def test_sorter_custom_objects_raises():
    # List with custom objects that are not comparable should raise TypeError
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        sorter(arr) # 44.4μs -> 42.6μs (4.20% faster)

def test_sorter_mixed_types_raises():
    # List with mixed types that can't be compared should raise TypeError
    arr = [1, 'a', 2]
    with pytest.raises(TypeError):
        sorter(arr) # 43.8μs -> 42.1μs (3.86% faster)

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

def test_sorter_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.3ms -> 37.1μs (54528% faster)

def test_sorter_large_reverse_sorted_list():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.1ms -> 36.3μs (90978% faster)

def test_sorter_large_random_integers():
    # Large random list of integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.1ms -> 71.3μs (42090% faster)

def test_sorter_large_random_floats():
    # Large random list of floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.7ms -> 295μs (9278% 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 # 31.0ms -> 113μs (27227% 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 # 27.0ms -> 59.6μs (45186% faster)

def test_sorter_large_already_sorted_strings():
    # Large already sorted list of strings
    arr = [f"item{str(i).zfill(4)}" for i in range(1000)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 22.7ms -> 45.5μs (49854% faster)

def test_sorter_large_reverse_sorted_strings():
    # Large reverse sorted list of strings
    arr = [f"item{str(i).zfill(4)}" for i in range(999, -1, -1)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 35.6ms -> 45.7μs (77762% 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-md7u2id2 and push.