⚡️ Speed up function sorter by 209,041%

[codeflash-ai]

📄 209,041% (2,090.41x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.37 seconds → 1.61 milliseconds (best of 582 runs)

📝 Explanation and details

Here is a rewritten, faster version of your program.
Optimizations:

• Uses Python's built-in sort() method, which is highly optimized and much faster than bubble sort.
• Avoids redundant sorting and memory by sorting in-place.
• Preserves comments and print statements.

This version is much faster, especially for large arrays.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 54 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	6.79ms	16.6μs	✅40764%
test_bubble_sort.py::test_sort	832ms	131μs	✅633939%
test_bubble_sort_conditional.py::test_sort	6.08μs	3.04μs	✅100.0%
test_bubble_sort_import.py::test_sort	830ms	132μs	✅625005%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	828ms	133μs	✅619249%
test_bubble_sort_parametrized.py::test_sort_parametrized	508ms	132μs	✅382384%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	102μs	21.5μs	✅377%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for non-integer sorting tests

# 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 # 5.29μs -> 3.12μs (69.3% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 2.96μs (70.5% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.00μs (65.3% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.42μs -> 3.17μs (71.1% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.17μs (63.2% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 2.92μs (38.6% faster)

def test_sorter_basic_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 3.00μs (36.1% faster)

def test_sorter_basic_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 2.79μs (44.8% faster)

def test_sorter_basic_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.00μs (48.6% faster)

def test_sorter_basic_floats():
    # List with floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.42μs -> 3.54μs (81.1% faster)

def test_sorter_basic_mixed_int_float():
    # List with integers and floats
    arr = [2, 3.5, 1, 2.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 3.50μs (60.7% faster)

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.12μs (57.3% faster)

def test_sorter_basic_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.71μs -> 2.92μs (27.1% faster)

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

def test_sorter_edge_large_negative_and_positive():
    # List with large negative and positive numbers
    arr = [1000000, -1000000, 0, 999999, -999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.54μs -> 3.25μs (70.5% faster)

def test_sorter_edge_min_max_int():
    # List with min and max int values
    import sys
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.25μs (56.4% faster)

def test_sorter_edge_all_negative():
    # All negative numbers
    arr = [-3, -5, -1, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 2.79μs (71.6% faster)

def test_sorter_edge_single_string():
    # Single string element
    arr = ["singleton"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.67μs -> 2.92μs (25.7% faster)

def test_sorter_edge_unicode_strings():
    # Unicode strings
    arr = ["ápple", "apple", "äpple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 3.46μs (55.4% faster)

def test_sorter_edge_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.17μs (61.8% faster)

def test_sorter_edge_case_sensitive_strings():
    # Case sensitivity in strings
    arr = ["apple", "Banana", "banana", "Apple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.17μs (61.8% faster)

def test_sorter_edge_mutation():
    # Ensure the function mutates the input list (in-place sort)
    arr = [3, 2, 1]
    sorter(arr) # 4.21μs -> 3.21μs (31.2% faster)

def test_sorter_edge_not_returning_new_list():
    # Ensure the returned list is the same object as input
    arr = [4, 2, 3]
    codeflash_output = sorter(arr); result = codeflash_output # 4.42μs -> 3.00μs (47.2% faster)

def test_sorter_edge_large_identical_elements():
    # Large list with all identical elements
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.1ms -> 27.2μs (66672% faster)

def test_sorter_edge_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.7ms -> 28.0μs (66723% faster)

def test_sorter_edge_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.5ms -> 28.0μs (108658% faster)

def test_sorter_edge_non_comparable():
    # List with non-comparable types should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.17μs -> 1.92μs (65.3% faster)

def test_sorter_edge_none_in_list():
    # List with None and numbers should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.62μs -> 1.92μs (36.9% faster)

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

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.1ms -> 57.8μs (50283% faster)

def test_sorter_large_random_floats():
    # Large list of random floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.9ms -> 266μs (10345% faster)

def test_sorter_large_random_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 # 30.0ms -> 88.2μs (33931% faster)

def test_sorter_large_duplicates_and_uniques():
    # Large list with many duplicates and some unique values
    arr = [5]*500 + [3]*300 + [7]*200
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 23.6ms -> 46.6μs (50582% faster)

def test_sorter_large_alternating():
    # Large list alternating between two values
    arr = [0, 1] * 500
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 23.2ms -> 44.3μs (52138% faster)

def test_sorter_large_strings_with_special_characters():
    # Large list of strings with special characters
    chars = string.ascii_letters + string.digits + "!@#$%^&*"
    arr = [''.join(random.choices(chars, k=8)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.5ms -> 99.7μs (29496% 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 scale test data
import sys  # used for edge case with max/min integers

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

# unit tests

# ----------------------
# 1. BASIC TEST CASES
# ----------------------

def test_sorter_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.67μs -> 2.92μs (25.7% faster)

def test_sorter_single_element():
    # Test sorting a list with one element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 3.04μs (28.8% 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 # 4.54μs -> 3.04μs (49.4% 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 # 5.08μs -> 3.08μs (64.9% faster)

def test_sorter_unsorted_list():
    # Test sorting a randomly unsorted list
    arr = [3, 1, 4, 2, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.12μs (56.0% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate values
    arr = [4, 2, 2, 5, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.00μs (73.6% 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()); result = codeflash_output # 5.17μs -> 3.12μs (65.3% 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 # 4.33μs -> 2.92μs (48.5% faster)

# ----------------------
# 2. EDGE TEST CASES
# ----------------------

def test_sorter_large_and_small_integers():
    # Test sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 1, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 3.42μs (76.8% faster)

def test_sorter_floats_and_integers():
    # Test sorting a list with both floats and integers
    arr = [3.5, 2, 4.1, 2.0, -1.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.54μs -> 3.92μs (67.1% faster)

def test_sorter_negative_and_positive_zero():
    # Test sorting a list with -0.0 and 0.0 (should be treated as equal in Python)
    arr = [0.0, -0.0, 1, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.58μs (45.4% faster)

def test_sorter_stability():
    # Test sorting stability: equal elements should retain their original order
    class Obj:
        def __init__(self, key, label):
            self.key = key
            self.label = label
        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.label == other.label
        def __repr__(self):
            return f"Obj({self.key}, '{self.label}')"
    a = Obj(1, 'a')
    b = Obj(2, 'b')
    c = Obj(1, 'c')
    arr = [a, b, c]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.29μs -> 4.21μs (49.5% faster)

def test_sorter_with_mutable_elements():
    # Test sorting a list of mutable elements (lists)
    arr = [[2], [1], [3]]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.38μs (45.7% faster)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.17μs (50.0% faster)

def test_sorter_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 3.58μs (70.9% faster)

def test_sorter_mixed_types_raises():
    # Test that sorting a list with incomparable types raises TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.62μs -> 1.92μs (37.0% faster)

# ----------------------
# 3. LARGE SCALE TEST CASES
# ----------------------

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list (performance)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.7ms -> 28.5μs (65436% 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 # 30.5ms -> 28.6μs (106580% faster)

def test_sorter_large_random_list():
    # Test sorting a large randomly shuffled list
    arr = list(range(1000))
    random.seed(42)  # deterministic shuffle
    random.shuffle(arr)
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.7ms -> 67.3μs (41081% 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 # 25.0ms -> 46.6μs (53654% faster)

def test_sorter_large_negative_and_positive():
    # Test sorting a large list with both negative and positive numbers
    arr = [random.randint(-500, 500) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.3ms -> 57.5μs (50777% 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-md9ft9ag and push.