⚡️ Speed up function sorter by 176,844%

[codeflash-ai]

📄 176,844% (1,768.44x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.36 seconds → 1.90 milliseconds (best of 532 runs)

📝 Explanation and details

Here’s a high-performance rewrite of your function.
Your original code uses a naive bubble sort (O(n²)), which is extremely slow for large lists.
Python’s built-in list.sort() uses Timsort (O(n log n) in practice), which is much faster and optimized in C.
Also, list.sort() is in-place, just like your version.

Optimized code:

Explanation of changes:

• Replaced the double for-loop with the built-in .sort() for optimal speed and reduced memory usage.
• Kept all function comments/outputs unchanged, except where the sort logic is optimized.

Result:

• Empirically, this will be orders of magnitude faster and also reduces the interpreter overhead.
• Output and return are exactly the same as your original function.

If you must provide your own sort algorithm (instead of using sort()), consider using a faster implementation like quicksort or heapsort. But in production Python, the built-in sort is always preferred for performance.

✅ 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	6.93ms	17.2μs	✅40093%
test_bubble_sort.py::test_sort	831ms	143μs	✅580446%
test_bubble_sort_conditional.py::test_sort	5.67μs	3.17μs	✅79.0%
test_bubble_sort_import.py::test_sort	827ms	139μs	✅593712%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	828ms	144μs	✅574839%
test_bubble_sort_parametrized.py::test_sort_parametrized	510ms	139μs	✅366839%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	98.8μs	21.2μs	✅366%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random test cases
import string  # used for string sorting test cases
import sys  # for edge cases with sys.maxsize

# 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_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.42μs -> 3.21μs (37.7% faster)

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.04μs (46.6% faster)

def test_sorter_unsorted_list():
    # Unsorted list with random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 3.04μs (43.8% faster)

def test_sorter_list_with_duplicates():
    # List containing duplicate values
    arr = [4, 2, 5, 2, 3, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.21μs (39.0% faster)

def test_sorter_single_element():
    # List with a single element should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.38μs -> 2.96μs (14.1% faster)

def test_sorter_two_elements_sorted():
    # Two element list, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.38μs -> 2.96μs (14.1% faster)

def test_sorter_two_elements_unsorted():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.71μs -> 2.92μs (27.2% faster)

def test_sorter_all_equal_elements():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.83μs -> 2.96μs (29.5% faster)

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, -5, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.21μs (49.3% faster)

def test_sorter_mixed_positive_and_negative():
    # List with both positive and negative numbers
    arr = [3, -1, 2, -5, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.17μs (44.7% faster)

# -------------------------------
# 2. EDGE TEST CASES
# -------------------------------

def test_sorter_empty_list():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.54μs -> 2.88μs (23.2% faster)

def test_sorter_large_numbers():
    # List with very large integers
    arr = [999999999, 12345678901234567890, -999999999, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.54μs (45.9% faster)

def test_sorter_min_max_int():
    # List with min and max integer values
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.33μs (42.5% faster)

def test_sorter_floats():
    # List with floating point numbers
    arr = [3.14, 2.71, -1.0, 0.0, 2.71]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 3.75μs (60.0% faster)

def test_sorter_mixed_int_float():
    # List with both ints and floats
    arr = [1, 2.0, 0, -3.5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 3.83μs (59.8% faster)

def test_sorter_strings():
    # List of strings (should sort lexicographically)
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.21μs (58.4% faster)

def test_sorter_strings_with_case():
    # List of strings with different cases
    arr = ["Banana", "apple", "Apple", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.17μs (48.7% faster)

def test_sorter_unicode_strings():
    # List of unicode strings
    arr = ["café", "cafe", "caff", "cafè"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.71μs -> 3.54μs (61.2% faster)

def test_sorter_list_of_lists():
    # List of lists (should sort by first element of each sublist)
    arr = [[2, 3], [1, 2], [2, 2], [1, 1]]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 3.92μs (49.0% faster)

def test_sorter_list_with_none():
    # List containing None should raise TypeError (since None cannot be compared with int)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

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

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

def test_sorter_empty_string_list():
    # List of empty strings
    arr = ["", "", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 3.12μs (38.7% faster)

def test_sorter_list_of_bools():
    # List of booleans (should sort as False < True)
    arr = [True, False, True, False]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.12μs (48.0% faster)

# -------------------------------
# 3. LARGE SCALE TEST CASES
# -------------------------------

def test_sorter_large_random_integers():
    # Large list of random integers (size 1000)
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.3ms -> 59.8μs (47330% faster)

def test_sorter_large_sorted_list():
    # Large already sorted list (size 1000)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.4ms -> 29.4μs (62391% faster)

def test_sorter_large_reverse_sorted_list():
    # Large reverse sorted list (size 1000)
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.4ms -> 30.0μs (101288% 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 # 24.8ms -> 49.3μs (50071% 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 # 29.7ms -> 90.5μs (32666% faster)

def test_sorter_large_floats():
    # 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 # 25.8ms -> 290μs (8799% 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 edge cases 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 # 4.79μs -> 3.04μs (57.6% 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 # 4.96μs -> 3.08μs (60.8% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.08μs (52.7% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.04μs (58.9% faster)

def test_sorter_basic_negatives():
    # List with negative numbers
    arr = [-2, -5, 3, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.08μs (46.0% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 3.04μs (28.8% faster)

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 2.96μs (35.2% faster)

def test_sorter_basic_two_elements_sorted():
    # Two element list, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.79μs -> 3.00μs (26.4% faster)

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

def test_sorter_edge_empty():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.58μs -> 2.79μs (28.3% faster)

def test_sorter_edge_all_same():
    # All elements the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 3.00μs (36.1% faster)

def test_sorter_edge_floats():
    # List with floats and integers
    arr = [3.1, 2, 5.5, 1, 4.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.83μs -> 3.75μs (82.2% faster)

def test_sorter_edge_large_and_small():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.67μs -> 3.50μs (90.5% faster)

def test_sorter_edge_strings():
    # List of strings should sort lexicographically
    arr = ['banana', 'apple', 'cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.17μs (60.6% faster)

def test_sorter_edge_strings_case():
    # List of strings with different cases
    arr = ['Banana', 'apple', 'Cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.17μs (59.2% faster)

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

def test_sorter_edge_nested_lists():
    # List with nested lists should raise TypeError
    arr = [1, [2, 3], 4]
    with pytest.raises(TypeError):
        sorter(arr.copy())

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

def test_sorter_edge_already_sorted_large_gap():
    # List with a large gap between two sorted segments
    arr = [1, 2, 3, 1000, 1001, 1002]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.25μs (60.2% 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 # 18.1ms -> 29.1μs (62189% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.4ms -> 30.0μs (101237% 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 # 28.8ms -> 59.4μs (48343% 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 # 24.7ms -> 49.0μs (50356% 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 # 29.7ms -> 91.0μs (32511% 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 # 27.2ms -> 283μs (9494% faster)

# ------------------- ADDITIONAL EDGE CASES -------------------

def test_sorter_edge_one_large_rest_small():
    # One large value, rest are small
    arr = [1]*999 + [1000000]
    expected = [1]*999 + [1000000]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 17.9ms -> 27.3μs (65547% faster)

def test_sorter_edge_one_small_rest_large():
    # One small value, rest are large
    arr = [1000000]*999 + [1]
    expected = [1] + [1000000]*999
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.6ms -> 33.8μs (54884% faster)

def test_sorter_edge_min_max_float():
    # List with min and max float values
    arr = [float('-inf'), 0.0, float('inf'), -1.0, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.62μs -> 3.58μs (84.9% faster)

def test_sorter_edge_sort_is_stable():
    # Sorting should be stable for equal elements
    class Item:
        def __init__(self, key, label):
            self.key = key
            self.label = label
        def __lt__(self, other):
            return self.key < other.key
        def __eq__(self, other):
            return self.key == other.key and self.label == other.label
        def __repr__(self):
            return f"Item({self.key}, {self.label})"
    arr = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c'), Item(2, 'd')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.62μs -> 4.46μs (71.0% faster)
    expected = [Item(1, 'a'), Item(1, 'c'), Item(2, 'b'), Item(2, 'd')]

def test_sorter_edge_large_negative_numbers():
    # List with large negative numbers
    arr = [-10**9, -1, -10**8, -5, -10**7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.54μs -> 3.38μs (64.2% 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-mc2j4vab and push.

[codeflash-ai]

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