⚡️ Speed up function sorter by 190,732%

[codeflash-ai]

📄 190,732% (1,907.32x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.86 seconds → 2.02 milliseconds (best of 140 runs)

📝 Explanation and details

You are currently using a naive bubble sort algorithm, which is O(n²). Python's built-in sort() method uses Timsort (O(n log n)) and is much faster both in CPU and wall clock time, as well as RAM usage, since it is written in C.

Below is an optimized version using the built-in sort, keeping all prints and return values the same and preserving all existing comments.

This version will have dramatically improved runtime and memory profile compared to the original. No functional change, only enhanced performance.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 50 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	7.53ms	21.9μs	✅34240%
test_bubble_sort.py::test_sort	965ms	155μs	✅621374%
test_bubble_sort_conditional.py::test_sort	11.2μs	8.00μs	✅40.6%
test_bubble_sort_import.py::test_sort	959ms	157μs	✅609819%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	965ms	160μs	✅600369%
test_bubble_sort_parametrized.py::test_sort_parametrized	597ms	158μs	✅375664%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	135μs	47.4μs	✅185%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for edge value 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_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.08μs -> 7.58μs (19.8% faster)

def test_sorter_single_element():
    # Test sorting a single-element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.42μs -> 7.67μs (22.8% 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 # 10.4μs -> 7.62μs (36.1% 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 # 10.3μs -> 7.62μs (35.0% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 7.54μs (39.8% 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 # 10.6μs -> 7.71μs (37.3% faster)

def test_sorter_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    arr = [5, -10, 0, 3, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 7.58μs (37.4% 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 # 9.67μs -> 8.42μs (14.9% faster)

def test_sorter_floats():
    # Test sorting a list of floats
    arr = [3.2, 1.5, 2.7, 1.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 8.46μs (39.9% faster)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.29μs (22.6% faster)

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

def test_sorter_large_integers():
    # Test sorting a list with very large integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 8.75μs (33.8% faster)
    expected = sorted(arr)

def test_sorter_min_max_floats():
    # Test sorting a list with min/max float values
    arr = [float('inf'), float('-inf'), 0.0, 1.1, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.0μs -> 8.46μs (41.4% faster)
    expected = [float('-inf'), -1.1, 0.0, 1.1, float('inf')]

def test_sorter_case_sensitive_strings():
    # Test sorting a list of strings with different cases
    arr = ["apple", "Banana", "banana", "Apple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.00μs (33.9% faster)
    expected = sorted(arr)

def test_sorter_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ["café", "cafe", "cafè", "cafÉ"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 8.42μs (39.1% faster)
    expected = sorted(arr)

def test_sorter_single_type_enforcement():
    # Test that sorting a list with mixed types raises TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_none_in_list():
    # Test that sorting a list with None and ints raises TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_mutable_input():
    # Test that the input list is sorted in-place (since the function mutates the input)
    arr = [3, 2, 1]
    sorter(arr) # 10.0μs -> 7.71μs (29.7% faster)

def test_sorter_all_negative_zero():
    # Test sorting a list of all zeros and negative zeros (floats)
    arr = [-0.0, 0.0, -0.0, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.08μs (25.8% faster)

def test_sorter_nan_values():
    # Test sorting a list with NaN values (should place NaNs at the end since NaN != NaN)
    arr = [3, float('nan'), 1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.92μs (17.3% faster)
    # NaN is always placed at the end by Python's sorted, but our bubble sort may not, so we check all non-NaN first
    non_nan = [x for x in result if x == x]
    nan_count = sum(1 for x in result if x != x)

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

def test_sorter_large_random_list():
    # Test sorting a large random list of integers
    arr = random.sample(range(-10000, -9000), 1000)  # 1000 unique integers
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.6ms -> 67.2μs (45370% 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 # 28.1ms -> 56.3μs (49849% 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 # 20.4ms -> 35.5μs (57304% faster)

def test_sorter_large_reverse_sorted():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.0ms -> 35.3μs (96244% faster)

def test_sorter_large_strings():
    # Test sorting a large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=8)) for _ in range(500)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.43ms -> 55.4μs (13317% 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/minsize 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_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.29μs -> 7.71μs (20.5% faster)

def test_sorter_single_element():
    # Test sorting a list with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 7.54μs (32.6% 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 # 10.8μs -> 7.71μs (39.5% 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 # 10.1μs -> 7.71μs (30.8% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    arr = [3, 1, 2, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 7.67μs (34.8% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 7.83μs (34.6% faster)

def test_sorter_mixed_signs():
    # Test sorting a list with both positive and negative numbers
    arr = [-10, 0, 5, -3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.75μs -> 7.58μs (28.6% 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 # 8.42μs -> 7.62μs (10.4% faster)

def test_sorter_floats():
    # Test sorting a list of floating point numbers
    arr = [3.1, 2.4, 5.6, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 8.17μs (49.0% faster)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 7.79μs (31.0% faster)

# 2. Edge Test Cases

def test_sorter_large_negative_and_positive():
    # Test sorting with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize - 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 8.04μs (20.7% faster)

def test_sorter_strings_with_empty_string():
    # Test sorting strings with empty string included
    arr = ["", "z", "a", " "]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 7.83μs (37.2% faster)

def test_sorter_mixed_case_strings():
    # Test sorting strings with different cases
    arr = ["Apple", "apple", "Banana", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.08μs (25.3% faster)

def test_sorter_special_characters():
    # Test sorting strings with special characters
    arr = ["#", "!", "@", "a", "A"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.00μs (31.2% faster)

def test_sorter_already_sorted_large():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.5ms -> 36.5μs (56164% faster)

def test_sorter_reverse_sorted_large():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.4ms -> 35.0μs (98339% faster)

def test_sorter_all_identical_large():
    # Test sorting a large list of identical elements
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.9ms -> 33.2μs (62763% faster)

def test_sorter_floats_and_ints():
    # Test sorting a list with both floats and ints
    arr = [1.5, 2, 0.5, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.6μs -> 8.96μs (51.6% faster)

def test_sorter_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 8.54μs (42.5% faster)

def test_sorter_mutation():
    # Test that the input list is mutated (in-place sort)
    arr = [3, 2, 1]
    sorter(arr) # 10.3μs -> 8.21μs (25.9% faster)

# 3. Large Scale Test Cases

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.6ms -> 67.5μs (45253% faster)

def test_sorter_large_random_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.9ms -> 300μs (9505% faster)

def test_sorter_large_random_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 # 32.3ms -> 99.5μs (32395% 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 # 27.7ms -> 56.4μs (49012% faster)

def test_sorter_large_alternating_pattern():
    # Test sorting a large list with an alternating pattern
    arr = [i % 2 for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.3ms -> 49.3μs (49236% faster)

def test_sorter_large_sorted_then_reverse():
    # Test sorting a list that's half sorted, half reverse sorted
    arr = list(range(500)) + list(range(999, 499, -1))
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.8ms -> 36.2μs (68247% 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-mcy07xlu and push.