⚡️ Speed up function sorter by 149,437%

[codeflash-ai]

📄 149,437% (1,494.37x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.77 seconds → 2.52 milliseconds (best of 105 runs)

📝 Explanation and details

Here is an optimized version of your program with a much faster sort (using Python’s built-in list.sort, which is implemented in C and highly optimized).
This preserves output messages and function signature, and keeps the same return value.
No unnecessary swapping, indexing, or allocation is done.

Notes about optimization.

• The original used a quadratic-time bubble sort, which is extremely slow for large lists.
• list.sort() is highly optimized in C and suitable for all "sort this list" cases except where you have a custom comparison logic.
• All function comments and prints are preserved.

This version will be orders of magnitude faster in both time and space for all realistic input sizes.

✅ 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	7.47ms	23.5μs	✅31647%
test_bubble_sort.py::test_sort	943ms	155μs	✅607733%
test_bubble_sort_conditional.py::test_sort	13.3μs	8.75μs	✅51.9%
test_bubble_sort_import.py::test_sort	945ms	156μs	✅603023%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	915ms	158μs	✅578202%
test_bubble_sort_parametrized.py::test_sort_parametrized	561ms	156μs	✅359039%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	138μs	54.9μs	✅152%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for maxsize in edge cases

# 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_sorted_integers():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 8.88μs (28.6% faster)

def test_sorter_reverse_integers():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 9.04μs (26.7% faster)

def test_sorter_unsorted_integers():
    # Unsorted list with positive integers
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 8.92μs (24.8% faster)

def test_sorter_duplicates():
    # List with duplicate elements
    arr = [4, 2, 5, 2, 3, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 8.67μs (32.7% faster)

def test_sorter_negative_integers():
    # List with negative and positive integers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 8.96μs (26.0% faster)

def test_sorter_single_element():
    # Single element list should return same list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 9.79μs (3.83% faster)

def test_sorter_two_elements_sorted():
    # Two element list already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.88μs (14.1% faster)

def test_sorter_two_elements_unsorted():
    # Two element list unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.46μs (20.7% faster)

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.88μs -> 8.67μs (13.9% faster)

def test_sorter_floats():
    # List with floats
    arr = [3.1, 2.2, 5.5, 4.4, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.8μs -> 9.96μs (28.0% faster)

def test_sorter_strings():
    # List with strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 9.08μs (25.7% faster)

def test_sorter_mixed_case_strings():
    # List with mixed case strings (lexicographical order)
    arr = ["Banana", "apple", "Cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 8.92μs (25.2% faster)

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

def test_sorter_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.71μs (22.5% faster)

def test_sorter_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.6μs -> 9.50μs (32.5% faster)

def test_sorter_strings_with_empty_string():
    # Strings with empty string included
    arr = ["z", "", "a", " "]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 8.88μs (28.2% faster)

def test_sorter_floats_and_integers():
    # List with both integers and floats
    arr = [3, 1.5, 2, 4.0, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.6μs -> 9.58μs (31.3% faster)

def test_sorter_list_with_none():
    # List with None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_list_with_unorderable_types():
    # List with unorderable types should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_list_with_nan():
    # List with float('nan') should sort, but nan is not equal to itself, so check order
    arr = [3, float('nan'), 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 9.46μs (18.1% faster)

def test_sorter_list_with_inf():
    # List with float('inf') and float('-inf')
    arr = [3, float('inf'), 1, float('-inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 9.17μs (29.1% faster)


def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.0ms -> 71.4μs (43336% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicate values
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.6ms -> 59.2μs (46489% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.4ms -> 36.6μs (55508% 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 # 33.7ms -> 36.5μs (92261% 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 # 31.8ms -> 100μs (31614% faster)

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.1ms -> 287μs (10387% 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 generating string test cases
import sys  # used for edge case with sys.maxsize

# 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 # 11.7μs -> 9.08μs (28.9% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.6μs -> 8.62μs (34.3% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 8.67μs (29.3% faster)

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 9.29μs (8.52% faster)

def test_sorter_basic_all_equal():
    # All elements the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.79μs (18.9% faster)

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 9.04μs (13.8% faster)

def test_sorter_basic_two_elements_sorted():
    # Two element list, sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 8.88μs (16.0% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.29μs -> 8.46μs (9.85% faster)

def test_sorter_basic_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.79μs -> 8.42μs (16.3% faster)

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

def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-1, -3, 2, 0, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 8.92μs (28.0% faster)

def test_sorter_edge_large_numbers():
    # List with very large numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 9.12μs (28.3% faster)

def test_sorter_edge_floats():
    # List with floats and integers
    arr = [3.2, 1, 4.5, 2.0, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.3μs -> 9.67μs (37.5% faster)

def test_sorter_edge_mixed_signs():
    # List with positive, negative, zero
    arr = [0, -1, 1, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 9.12μs (21.5% faster)

def test_sorter_edge_strings():
    # List of strings (alphabetical order)
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 9.04μs (18.9% faster)

def test_sorter_edge_strings_with_duplicates():
    # List of strings with duplicates
    arr = ['apple', 'banana', 'apple']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.71μs (25.8% faster)

def test_sorter_edge_empty_strings():
    # List with empty strings
    arr = ['', 'a', '', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 9.29μs (22.0% faster)

def test_sorter_edge_case_sensitive_strings():
    # List with case-sensitive strings
    arr = ['a', 'A', 'b', 'B']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 9.42μs (19.0% faster)

def test_sorter_edge_unicode_strings():
    # List with unicode characters
    arr = ['é', 'e', 'ê', 'ë']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 9.38μs (24.9% faster)

def test_sorter_edge_list_of_lists():
    # List of lists (should sort by first element of each sublist)
    arr = [[2, 3], [1, 4], [2, 2]]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 9.29μs (26.0% faster)

def test_sorter_edge_list_of_tuples():
    # List of tuples (should sort by first element of each tuple)
    arr = [(2, 3), (1, 4), (2, 2)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 9.71μs (18.9% faster)

def test_sorter_edge_heterogeneous_types():
    # List with different types should raise TypeError
    arr = [1, 'a', 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_none_in_list():
    # List with None and integers should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_nested_empty_lists():
    # List with empty inner lists
    arr = [[], [1], [0]]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 9.12μs (24.2% faster)

def test_sorter_edge_boolean_values():
    # List with boolean values (False < True)
    arr = [True, False, True]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.75μs -> 8.79μs (10.9% 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 # 20.7ms -> 37.5μs (55226% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.1ms -> 36.9μs (92306% faster)

def test_sorter_large_random():
    # Large random list of integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.9ms -> 72.0μs (41500% 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 # 27.3ms -> 58.9μs (46339% 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 # 31.8ms -> 98.3μs (32259% faster)

def test_sorter_large_all_equal():
    # Large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.9ms -> 34.0μs (58546% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.8ms -> 288μs (10213% faster)

def test_sorter_large_negative_and_positive():
    # Large list with negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.4ms -> 71.5μs (42422% 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-mcmib6jd and push.