⚡️ Speed up function sorter by 140,926%

[codeflash-ai]

📄 140,926% (1,409.26x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.52 seconds → 2.50 milliseconds (best of 112 runs)

📝 Explanation and details

Here’s a faster version using Python’s built-in sort method. The original code did a bubble sort, which is O(n²); the built-in sort is Timsort (O(n log n)), making it much faster and using less memory by not making unnecessary variable copies.

All print statements and return values are preserved.

This will give identical output and results, but with significantly improved speed and lower memory usage.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 59 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	7.62ms	22.9μs	✅33217%
test_bubble_sort.py::test_sort	876ms	158μs	✅552911%
test_bubble_sort_conditional.py::test_sort	11.8μs	8.46μs	✅39.9%
test_bubble_sort_import.py::test_sort	875ms	157μs	✅556436%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	884ms	159μs	✅554475%
test_bubble_sort_parametrized.py::test_sort_parametrized	539ms	158μs	✅340608%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	136μs	49.9μs	✅174%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for generating lists of 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_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.62μs -> 8.96μs (7.45% faster)

def test_sorter_single_element():
    # Test sorting a list with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.92μs -> 7.88μs (25.9% 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 # 10.2μs -> 8.12μs (25.6% 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 # 10.8μs -> 8.12μs (32.8% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.25μs (29.8% faster)

def test_sorter_list_with_duplicates():
    # Test sorting a list with duplicate elements
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 9.12μs (20.1% 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 # 10.8μs -> 8.29μs (30.7% faster)

def test_sorter_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    arr = [5, -10, 3, 0, -2, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 8.79μs (29.9% faster)

def test_sorter_all_equal_elements():
    # Test sorting a list where all elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.04μs (25.9% faster)

def test_sorter_floats():
    # Test sorting a list of floats
    arr = [2.5, 3.1, 0.5, 2.5, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.9μs -> 8.88μs (45.1% faster)

def test_sorter_strings():
    # Test sorting a list of strings lexicographically
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 8.79μs (24.2% faster)

def test_sorter_case_sensitive_strings():
    # Test sorting a list of strings with different cases
    arr = ["banana", "Apple", "cherry", "Date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.38μs (27.9% faster)

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

def test_sorter_large_negative_and_positive():
    # Test sorting a list with very large and very small integers
    arr = [2**31-1, -2**31, 0, 99999, -99999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 9.67μs (25.9% faster)

def test_sorter_floats_and_ints():
    # Test sorting a list with both floats and ints
    arr = [1, 3.5, 2, 2.0, -1.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.3μs -> 9.92μs (23.9% faster)

def test_sorter_single_large_value():
    # Test sorting a list with only one very large value
    arr = [999999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 8.08μs (20.1% faster)

def test_sorter_list_with_none_raises():
    # Test sorting a list with None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 42.2μs -> 40.2μs (5.08% faster)

def test_sorter_list_with_unorderable_types_raises():
    # Test sorting a list with unorderable types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 42.9μs -> 40.9μs (4.79% faster)

def test_sorter_list_with_nested_lists_raises():
    # Test sorting a list with nested lists should raise TypeError
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 42.5μs -> 42.2μs (0.790% faster)

def test_sorter_list_with_nan():
    # Test sorting a list with float('nan') values
    arr = [1, float('nan'), 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.88μs (20.7% faster)

def test_sorter_list_with_infinity():
    # Test sorting a list with float('inf') and float('-inf')
    arr = [1, float('inf'), -1, float('-inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 8.58μs (31.6% faster)

def test_sorter_list_with_zeros():
    # Test sorting a list with positive and negative zeros
    arr = [0, -0.0, 0.0, -0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.38μs (25.9% faster)

def test_sorter_list_with_long_strings():
    # Test sorting a list with very long strings
    arr = ["a"*100, "b"*50, "a"*99]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.9μs -> 9.75μs (22.2% faster)

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

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers (size 1000)
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.9ms -> 71.5μs (41681% faster)

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list (size 1000)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.0ms -> 34.5μs (57821% faster)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list (size 1000)
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.1ms -> 34.9μs (94576% faster)

def test_sorter_large_list_with_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 # 26.3ms -> 57.6μs (45499% faster)

def test_sorter_large_list_of_strings():
    # Test sorting a 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 # 31.4ms -> 100μs (31191% faster)

def test_sorter_large_list_with_negative_and_positive_floats():
    # Test sorting a large list of floats with both negative and positive values
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.4ms -> 294μs (9566% 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 testing with very 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_basic_sorted():
    # Already sorted list should remain the same
    arr = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 8.12μs (30.8% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 8.04μs (37.8% faster)

def test_sorter_basic_unsorted():
    # Random order list
    arr = [3, 1, 4, 5, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.08μs (32.5% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 4, 1]
    expected = [1, 1, 2, 2, 3, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 8.12μs (37.4% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    expected = [-3, -2, -1, 0, 1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.38μs (25.9% faster)

def test_sorter_basic_floats():
    # List with floats
    arr = [1.1, 2.2, 0.5, -1.0, 3.3]
    expected = [-1.0, 0.5, 1.1, 2.2, 3.3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.2μs -> 9.00μs (47.2% faster)

def test_sorter_basic_strings():
    # List of strings (lexicographical order)
    arr = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 7.96μs (31.9% faster)

def test_sorter_basic_mixed_case_strings():
    # List of strings with mixed case
    arr = ["Banana", "apple", "Cherry"]
    expected = ["Banana", "Cherry", "apple"]  # Python's default sort is case-sensitive
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.88μs -> 8.08μs (9.80% faster)

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

def test_sorter_edge_empty_list():
    # Empty list should return empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.33μs -> 7.83μs (19.1% faster)

def test_sorter_edge_single_element():
    # Single element list should return the same list
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.50μs -> 8.12μs (16.9% faster)

def test_sorter_edge_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7, 7]
    expected = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.00μs (28.1% faster)

def test_sorter_edge_already_sorted_with_duplicates():
    # Already sorted list with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    expected = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.54μs -> 7.96μs (19.9% faster)

def test_sorter_edge_large_negative_and_positive():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    expected = [-sys.maxsize-1, -999999999, 0, 999999999, sys.maxsize]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 8.38μs (39.3% faster)

def test_sorter_edge_all_negative():
    # All negative numbers
    arr = [-5, -10, -3, -1]
    expected = [-10, -5, -3, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 8.04μs (31.6% faster)

def test_sorter_edge_floats_and_ints():
    # Mix of floats and ints
    arr = [1, 3.5, 2, 0.5, -1]
    expected = [-1, 0.5, 1, 2, 3.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.2μs -> 9.38μs (41.3% faster)

def test_sorter_edge_unicode_strings():
    # Unicode strings
    arr = ["ápple", "apple", "äpple"]
    expected = ["apple", "ápple", "äpple"]  # Python's default sort order for unicode
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.83μs (18.9% faster)

def test_sorter_edge_strings_with_numbers():
    # Strings that contain numbers
    arr = ["10", "2", "1"]
    expected = ["1", "10", "2"]  # Lexicographical order, not numeric
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.42μs (26.7% faster)

def test_sorter_edge_mutation():
    # Ensure the function sorts in-place and returns the same object (for lists)
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 10.3μs -> 8.25μs (25.2% faster)

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

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.9ms -> 36.2μs (54751% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.1ms -> 35.9μs (91925% 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 # 30.5ms -> 70.6μs (43117% 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 # 26.1ms -> 57.0μs (45754% 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 # 31.1ms -> 100μs (30831% faster)

def test_sorter_large_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 # 28.8ms -> 289μs (9853% faster)

# ----------------------------
# Error Handling / Type Robustness
# ----------------------------

def test_sorter_type_error_on_unorderable():
    # List with unorderable types (should raise TypeError)
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.6μs -> 41.6μs (4.70% faster)

def test_sorter_type_error_on_none():
    # List with None and numbers (should raise TypeError)
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 45.1μs -> 42.0μs (7.24% faster)

def test_sorter_type_error_on_dicts():
    # List with dictionaries (should raise TypeError)
    arr = [{"a": 1}, {"b": 2}]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 42.8μs -> 41.5μs (3.11% faster)

# ----------------------------
# Miscellaneous
# ----------------------------

def test_sorter_does_not_return_new_list():
    # Ensure the function does not return a new list object (in-place sort)
    arr = [4, 3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 11.0μs -> 7.96μs (37.7% faster)

def test_sorter_idempotency():
    # Sorting an already sorted list should not change it
    arr = [1, 2, 3, 4]
    codeflash_output = sorter(arr.copy()); result1 = codeflash_output # 10.2μs -> 7.88μs (29.1% faster)
    codeflash_output = sorter(result1); result2 = codeflash_output # 8.29μs -> 7.46μs (11.2% faster)

def test_sorter_stability():
    # Check for stability: equal elements should retain their order (for objects)
    class Item:
        def __init__(self, value, label):
            self.value = value
            self.label = label
        def __lt__(self, other):
            return self.value < other.value
        def __gt__(self, other):
            return self.value > other.value
        def __eq__(self, other):
            return self.value == other.value and self.label == other.label
        def __repr__(self):
            return f"Item({self.value}, '{self.label}')"
    arr = [Item(1, 'a'), Item(2, 'x'), Item(1, 'b'), Item(2, 'y')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.6μs -> 14.8μs (33.1% 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-md6jov5m and push.