⚡️ Speed up function sorter by 207,897%

[codeflash-ai]

📄 207,897% (2,078.97x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.36 seconds → 1.62 milliseconds (best of 579 runs)

📝 Explanation and details

Here is an optimized version of your program.
Your original code uses a naïve bubble sort, which is highly inefficient (O(n²)).
Python has a highly optimized built-in sort, Timsort, running in O(n log n).
Replacing the manual sort with list.sort() (in-place sort) maintains functionality and gives a huge performance boost.

All function signatures and return results are preserved, as are your print statements.
This change preserves the intended output and greatly improves speed, especially for large lists.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 53 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	6.92ms	16.9μs	✅40791%
test_bubble_sort.py::test_sort	832ms	138μs	✅602851%
test_bubble_sort_conditional.py::test_sort	6.21μs	3.21μs	✅93.5%
test_bubble_sort_import.py::test_sort	833ms	143μs	✅579460%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	832ms	142μs	✅583202%
test_bubble_sort_parametrized.py::test_sort_parametrized	507ms	144μs	✅350473%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	104μs	20.6μs	✅405%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random test cases
import string  # used for string-based test cases
import sys  # used for maxsize edge case

# 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 # 3.83μs -> 2.92μs (31.4% faster)

def test_sorter_single_element():
    # Test sorting a list with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 2.96μs (36.6% 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 # 4.67μs -> 3.08μs (51.3% 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 # 5.12μs -> 2.96μs (73.2% 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 # 4.71μs -> 2.96μs (59.1% faster)

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate values
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 2.96μs (74.6% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [0, -1, -3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.04μs (60.3% faster)

def test_sorter_floats():
    # Test sorting a list with float values
    arr = [3.2, 1.5, 2.8, 1.2, 3.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.71μs -> 3.62μs (85.0% faster)

def test_sorter_mixed_ints_floats():
    # Test sorting a list with both ints and floats
    arr = [2, 3.5, 1, 2.2, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 3.42μs (75.6% faster)

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

def test_sorter_all_equal_elements():
    # Test sorting a list where all elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 2.88μs (52.2% faster)

def test_sorter_large_and_small_numbers():
    # Test sorting a 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 # 6.29μs -> 3.12μs (101% faster)
    expected = [-sys.maxsize - 1, -999999999, 0, 999999999, sys.maxsize]

def test_sorter_strings():
    # Test sorting a list of strings (lexicographical order)
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.38μs (53.1% 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 # 4.88μs -> 3.12μs (56.0% faster)

def test_sorter_empty_strings():
    # Test sorting a list with empty strings
    arr = ["", "a", "abc", "", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 3.21μs (64.9% faster)

def test_sorter_special_characters():
    # Test sorting a list with special characters
    arr = ["!", "@", "#", "$", "%", "^", "&", "*", "A", "a"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.21μs -> 3.58μs (101% 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 # 18.4ms -> 30.2μs (61048% 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 # 30.6ms -> 30.1μs (101749% faster)

def test_sorter_mutates_input():
    # Test that the input list is mutated (since the implementation is in-place)
    arr = [3, 2, 1]
    sorter(arr)

def test_sorter_nan_and_inf():
    # Test sorting a list with float('inf'), float('-inf'), and float('nan')
    arr = [float('nan'), 1, float('inf'), -1, float('-inf'), 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.75μs -> 3.79μs (78.0% faster)
    # NaN is always considered not equal to anything, so it should end up at the end
    expected = [float('-inf'), -1, 0, 1, float('inf'), float('nan')]

def test_sorter_heterogeneous_types_error():
    # Test sorting a list with incompatible types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

# ------------------------
# 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)  # 1000 unique random ints
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.5ms -> 59.2μs (47926% 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 # 26.7ms -> 290μs (9099% faster)

def test_sorter_large_random_strings():
    # Test sorting a large list of random strings
    arr = [
        ''.join(random.choices(string.ascii_letters + string.digits, k=8))
        for _ in range(1000)
    ]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.9ms -> 97.8μs (30429% 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 # 24.8ms -> 49.4μs (50181% faster)

def test_sorter_large_already_sorted_with_duplicates():
    # Test sorting a large already sorted list with duplicates
    arr = sorted([random.choice([1, 2, 3, 4, 5]) for _ in range(1000)])
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.4ms -> 26.9μs (68303% faster)

def test_sorter_large_negative_numbers():
    # Test sorting a large list of negative numbers
    arr = [random.randint(-10000, -1) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.6ms -> 61.5μs (44755% 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 random lists
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

# ------------------------
# Basic Test Cases
# ------------------------

def test_sorter_empty_list():
    # Test that sorting an empty list returns an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 2.96μs (52.1% faster)

def test_sorter_single_element():
    # Test that sorting a single-element list returns the same list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.12μs -> 3.08μs (33.8% faster)

def test_sorter_sorted_list():
    # Test that a list already sorted remains unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.00μs (58.3% faster)

def test_sorter_reverse_sorted_list():
    # Test that a reverse-sorted list is sorted correctly
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 2.92μs (74.3% faster)

def test_sorter_unsorted_list():
    # Test that a randomly unsorted list is sorted correctly
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.04μs (47.9% faster)

def test_sorter_with_duplicates():
    # Test that a list with duplicate elements is sorted correctly
    arr = [4, 2, 2, 5, 3, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 3.12μs (74.7% faster)

def test_sorter_negative_numbers():
    # Test that a list with negative numbers is sorted correctly
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.17μs (47.4% faster)

def test_sorter_all_equal_elements():
    # Test that a list with all equal elements remains unchanged
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.96μs (31.0% faster)

def test_sorter_mixed_integers():
    # Test that a list with positive, negative, and zero is sorted
    arr = [0, -5, 3, -1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 3.08μs (40.5% faster)

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

def test_sorter_large_and_small_integers():
    # Test with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.92μs -> 3.33μs (77.4% faster)

def test_sorter_already_sorted_with_duplicates():
    # Test with a sorted list containing duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.12μs (46.7% faster)

def test_sorter_all_negative_duplicates():
    # Test with all negative numbers, some duplicates
    arr = [-2, -3, -2, -1, -3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.04μs (60.3% faster)

def test_sorter_two_elements_sorted():
    # Test with two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.96μs (31.0% faster)

def test_sorter_two_elements_unsorted():
    # Test with two elements in reverse order
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 2.83μs (41.2% faster)

def test_sorter_floats_and_integers():
    # Test with a mix of floats and integers
    arr = [3.2, 1, 2.5, 2, 1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.17μs -> 3.58μs (100.0% faster)

def test_sorter_with_zeroes():
    # Test with multiple zeroes in the list
    arr = [0, 0, 1, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.04μs (69.9% faster)

def test_sorter_minimal_variation():
    # Test with a list where all but one element are the same
    arr = [5, 5, 5, 3, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 3.04μs (43.9% faster)

def test_sorter_long_sorted_prefix():
    # Test with a list that is sorted except for the last element
    arr = [1, 2, 3, 4, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.04μs (60.3% faster)

def test_sorter_long_sorted_suffix():
    # Test with a list that is sorted except for the first element
    arr = [5, 1, 2, 3, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.00μs (54.2% faster)

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

def test_sorter_large_random_list():
    # Test with a large random list (size 1000)
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.1ms -> 61.0μs (45962% faster)

def test_sorter_large_sorted_list():
    # Test with a large already sorted list (size 1000)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.6ms -> 29.2μs (63788% faster)

def test_sorter_large_reverse_sorted_list():
    # Test with a large reverse sorted list (size 1000)
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.8ms -> 29.0μs (106063% faster)

def test_sorter_large_all_same_elements():
    # Test with a large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.3ms -> 27.8μs (65884% faster)

def test_sorter_large_many_duplicates():
    # Test with 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 # 24.9ms -> 48.5μs (51307% faster)

def test_sorter_large_alternating_pattern():
    # Test with a large list of alternating values
    arr = [i % 2 for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 21.7ms -> 43.8μs (49436% faster)

# ------------------------
# Mutation Testing Guards
# ------------------------

def test_sorter_mutation_guard_not_inplace():
    # Ensure that the function can handle a copy and does not mutate the input list outside
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr_copy); result = codeflash_output # 4.96μs -> 3.21μs (54.6% faster)

def test_sorter_mutation_guard_return_value():
    # Ensure that the function returns the sorted list, not just sorts in-place
    arr = [8, 7, 6]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 2.96μs (47.9% 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-mc2jicjp and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #321 by misrasaurabh1 was closed.