⚡️ Speed up function sorter by 183,490%

[codeflash-ai]

📄 183,490% (1,834.90x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.33 seconds → 1.81 milliseconds (best of 542 runs)

📝 Explanation and details

Here’s a much faster rewrite of your sorter function. Your code implements bubble sort (O(n^2)), which is very inefficient for non-trivial inputs. The most optimized method is to use Python's built-in sort()—it uses Timsort (O(n log n)), is implemented in C, and is much faster than any pure Python implementation for general arrays.

We’ll keep the output and function signature the same, only changing the sort implementation.

Notes:

• Built-in .sort() is fast, stable, and sorts in-place, preserving the semantics of your original function (arr modified in-place; same reference returned).
• All comments are preserved, as required.

This will dramatically improve performance for all but tiny arrays.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 60 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	6.88ms	16.7μs	✅41050%
test_bubble_sort.py::test_sort	833ms	143μs	✅582322%
test_bubble_sort_conditional.py::test_sort	5.75μs	3.21μs	✅79.2%
test_bubble_sort_import.py::test_sort	837ms	142μs	✅587569%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	834ms	144μs	✅577575%
test_bubble_sort_parametrized.py::test_sort_parametrized	508ms	142μs	✅356180%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	100μs	21.5μs	✅369%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for sys.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_simple_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.54μs -> 3.08μs (47.3%)

def test_sorter_simple_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.04μs (60.3%)

def test_sorter_simple_unsorted():
    # Unsorted list with positive numbers
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.12μs (46.7%)

def test_sorter_with_duplicates():
    # List with duplicate elements
    arr = [4, 2, 2, 3, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.42μs (45.1%)

def test_sorter_with_negative_numbers():
    # List with negative and positive numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.12μs (56.0%)

def test_sorter_with_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.92μs (28.6%)

def test_sorter_with_two_elements():
    # List with two elements
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 3.04μs (27.4%)

def test_sorter_with_all_equal_elements():
    # List where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.17μs -> 3.00μs (38.9%)

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

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.50μs -> 2.83μs (23.5%)

def test_sorter_large_numbers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 3.38μs (81.5%)

def test_sorter_with_floats():
    # List with floats and integers
    arr = [3.2, 1, 2.5, 0, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.12μs -> 3.79μs (87.9%)

def test_sorter_with_strings():
    # List with strings (lexicographical order)
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.17μs (51.3%)

def test_sorter_with_mixed_types_raises():
    # List with mixed types (should raise TypeError)
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_with_empty_strings():
    # List with empty strings
    arr = ["", "b", "a"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.29μs (35.5%)

def test_sorter_with_unicode_strings():
    # List with unicode strings
    arr = ["café", "apple", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.38μs (53.1%)

def test_sorter_with_bool_values():
    # List with boolean values (False < True)
    arr = [True, False, True]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.21μs (40.3%)

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

def test_sorter_stability():
    # Test that sorting is stable (relative order of equal elements is preserved)
    # We'll use tuples (value, original_index)
    arr = [(2, 'a'), (1, 'b'), (2, 'c'), (1, 'd')]
    # Sort by first element
    expected = [(1, 'b'), (1, 'd'), (2, 'a'), (2, 'c')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.17μs -> 4.00μs (54.2%)

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

def test_sorter_large_random_int_list():
    # Large list of random integers (size < 1000)
    arr = [random.randint(-10000, 10000) for _ in range(999)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.4ms -> 62.5μs (45337%)

def test_sorter_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.6ms -> 29.9μs (62128%)

def test_sorter_large_reverse_sorted_list():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.8ms -> 29.5μs (104195%)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [3] * 250 + [7] * 249
    expected = [3] * 250 + [5] * 500 + [7] * 249
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 21.7ms -> 27.1μs (79758%)

def test_sorter_large_string_list():
    # Large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(800)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.8ms -> 74.3μs (25125%)

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-10000, 10000) for _ in range(999)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.5ms -> 279μs (9740%)

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

@pytest.mark.parametrize("arr,expected", [
    ([2, 1, 3], [1, 2, 3]),         # basic
    ([1, 2, 3], [1, 2, 3]),         # already sorted
    ([3, 2, 1], [1, 2, 3]),         # reverse
    ([1, 1, 1], [1, 1, 1]),         # all equal
    ([0], [0]),                     # single
    ([1, 2, 2, 3], [1, 2, 2, 3]),   # duplicates
    ([-1, 0, 1], [-1, 0, 1]),       # negatives
])
def test_sorter_mutation_guard(arr, expected):
    # Ensures that any mutation in sorting logic will break at least one test
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.67μs -> 2.83μs (29.4%)
# 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 test cases
import string  # used for string sorting test cases
import sys  # used for sys.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_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 5.38μs -> 3.17μs (69.7%)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 5.29μs -> 3.12μs (69.3%)

def test_sorter_basic_random():
    # Random order list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.88μs -> 3.00μs (62.5%)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 4]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.71μs -> 3.04μs (54.8%)

def test_sorter_basic_negatives():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.75μs -> 3.08μs (54.1%)

def test_sorter_basic_mixed():
    # List with negative, zero, and positive numbers
    arr = [0, -1, 5, -10, 3]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 5.17μs -> 3.12μs (65.3%)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.00μs -> 2.96μs (35.2%)

def test_sorter_basic_two_elements_sorted():
    # List with two already sorted elements
    arr = [1, 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.83μs -> 2.92μs (31.4%)

def test_sorter_basic_two_elements_unsorted():
    # List with two unsorted elements
    arr = [2, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.83μs -> 3.00μs (27.8%)

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

def test_sorter_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.67μs -> 2.83μs (29.4%)

def test_sorter_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.46μs -> 2.96μs (50.7%)

def test_sorter_large_numbers():
    # List with very large numbers
    arr = [sys.maxsize, -sys.maxsize, 0, 999999999, -999999999]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 6.42μs -> 3.62μs (77.0%)

def test_sorter_small_numbers():
    # List with very small numbers (negative and positive)
    arr = [-1000000000, 0, 1000000000]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.46μs -> 3.33μs (33.8%)

def test_sorter_floats():
    # List with floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0, 4.8]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 6.71μs -> 3.83μs (75.0%)

def test_sorter_floats_and_ints():
    # List with both ints and floats
    arr = [3, 2.2, 5, 1.0, 4]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 6.08μs -> 3.75μs (62.2%)

def test_sorter_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.79μs -> 3.46μs (38.5%)

def test_sorter_empty_strings():
    # List with empty strings
    arr = ["", "a", "abc", ""]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.88μs -> 3.21μs (52.0%)

def test_sorter_single_char_strings():
    # List with single character strings
    arr = ["b", "a", "c", "a"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.75μs -> 3.21μs (48.1%)

def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["α", "β", "γ", "α"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 6.12μs -> 3.67μs (67.1%)

def test_sorter_mixed_types_raises():
    # List with mixed types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr[:])

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

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

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[:]); result = codeflash_output # 28.2ms -> 61.6μs (45736%)

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[:]); result = codeflash_output # 24.8ms -> 51.0μs (48583%)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr[:]); result = codeflash_output # 18.5ms -> 29.2μs (63219%)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr[:]); result = codeflash_output # 30.9ms -> 29.2μs (105622%)

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[:]); result = codeflash_output # 29.7ms -> 94.1μs (31473%)

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[:]); result = codeflash_output # 27.3ms -> 280μs (9623%)

# --------------------- MUTATION TESTING CASES ---------------------

def test_sorter_not_inplace():
    # Ensure the function does not modify the input list (since it does, this will fail if implementation changes)
    arr = [3, 2, 1]
    arr_copy = arr[:]
    sorter(arr)

def test_sorter_stability():
    # Test for stability: equal elements retain their relative order
    class StableObj:
        def __init__(self, val, tag):
            self.val = val
            self.tag = tag
        def __lt__(self, other):
            return self.val < other.val
        def __eq__(self, other):
            return self.val == other.val and self.tag == other.tag
        def __repr__(self):
            return f"StableObj({self.val}, {self.tag})"
    arr = [StableObj(1, 'a'), StableObj(1, 'b'), StableObj(2, 'c')]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 7.00μs -> 3.96μs (76.8%)
# 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-mc2ibb75 and push.

[codeflash-ai]

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