⚡️ Speed up function sorter by 72%

[codeflash-ai]

📄 72% (0.72x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.65 seconds → 2.12 seconds (best of 5 runs)

📝 Explanation and details

Here is a much faster version of your function. Your code is currently an unoptimized bubble sort (O(n²)), repeatedly swapping elements even when the list is already sorted, and iterating unnecessarily.
We can massively improve its speed by either (a) using Python’s built-in sort (Timsort, O(n log n)), or (b) at the very least limiting swaps using a flag, and range(len(arr) - i - 1) for the bubble sort (which is still much slower than Timsort).

Given your profiling result (CPU-bound; mostly swaps and comparisons), the optimal code is simply.

This is orders of magnitude faster than bubble sort, conserves memory, and keeps your function signature and output exactly as before.

If, for teaching purposes, you want a faster bubble sort, you can minimally optimize as.

But use the first version for real code; it is by far the fastest, and is standard Python.

---

Summary: Use the first version for maximum speed and minimal memory, taking native advantage of Python's Timsort.
All required comments are preserved.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 55 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	7.57ms	5.04ms	✅50.1%
test_bubble_sort.py::test_sort	894ms	639ms	✅40.0%
test_bubble_sort_conditional.py::test_sort	12.9μs	11.5μs	✅12.0%
test_bubble_sort_import.py::test_sort	900ms	635ms	✅41.8%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	885ms	635ms	✅39.4%
test_bubble_sort_parametrized.py::test_sort_parametrized	551ms	264μs	✅208139%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	139μs	59.9μs	✅133%

🌀 Generated Regression Tests and Runtime

import random  # used for large scale randomized tests
import string  # used for string sorting tests
import sys  # used for edge numeric limits

# 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]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 10.7μs -> 10.1μs (6.21% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 11.0μs -> 10.7μs (2.74% faster)

def test_sorter_basic_unsorted():
    # Unsorted list with positive integers
    arr = [3, 1, 4, 5, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 10.6μs -> 10.8μs (1.55% slower)

def test_sorter_basic_duplicates():
    # List with duplicate values
    arr = [2, 3, 2, 1, 4, 1]
    expected = [1, 1, 2, 2, 3, 4]
    codeflash_output = sorter(arr.copy()) # 11.0μs -> 11.0μs (0.000% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-1, -3, 2, 0, 1]
    expected = [-3, -1, 0, 1, 2]
    codeflash_output = sorter(arr.copy()) # 10.6μs -> 10.5μs (0.398% faster)

def test_sorter_basic_floats():
    # List with floats and integers
    arr = [2.5, 1.1, 3, 2, 1.0]
    expected = [1.0, 1.1, 2, 2.5, 3]
    codeflash_output = sorter(arr.copy()) # 11.6μs -> 12.5μs (7.30% slower)

def test_sorter_basic_strings():
    # List of strings should be sorted lexicographically
    arr = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(arr.copy()) # 10.2μs -> 10.2μs (0.410% slower)

def test_sorter_basic_mixed_case_strings():
    # Strings with different cases, upper-case comes before lower-case in ASCII
    arr = ["banana", "Apple", "cherry"]
    expected = ["Apple", "banana", "cherry"]
    codeflash_output = sorter(arr.copy()) # 9.12μs -> 10.2μs (11.0% slower)

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

def test_sorter_empty():
    # Empty list should return empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()) # 9.29μs -> 9.46μs (1.76% slower)

def test_sorter_single_element():
    # Single element list should return same list
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()) # 9.46μs -> 9.71μs (2.56% slower)

def test_sorter_all_equal():
    # All elements equal
    arr = [7, 7, 7, 7]
    expected = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()) # 10.2μs -> 9.79μs (4.26% faster)

def test_sorter_two_elements_sorted():
    # Two elements already sorted
    arr = [1, 2]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 9.50μs -> 9.71μs (2.14% slower)

def test_sorter_two_elements_unsorted():
    # Two elements unsorted
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 9.92μs -> 9.96μs (0.412% slower)

def test_sorter_large_negative_and_positive():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 12.3μs -> 11.4μs (7.66% faster)

def test_sorter_strings_with_empty_string():
    # List of strings with empty string
    arr = ["", "a", "abc", "A"]
    expected = ["", "A", "a", "abc"]
    codeflash_output = sorter(arr.copy()) # 10.7μs -> 10.8μs (0.778% slower)

def test_sorter_floats_edge():
    # List with float('inf'), float('-inf'), float('nan')
    arr = [1, float('inf'), -1, float('-inf'), 0]
    # NaN is always placed at the end when sorted in Python
    arr_with_nan = arr + [float('nan')]
    expected = sorted(arr)
    # For nan, comparison always returns False, so it stays at the end
    codeflash_output = sorter(arr_with_nan.copy()); result = codeflash_output # 13.1μs -> 12.2μs (7.55% faster)

def test_sorter_mixed_types_raises():
    # List with mixed types (int and str) should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.8μs -> 44.9μs (2.60% slower)

def test_sorter_mutation():
    # Ensure the function sorts in-place (the original list is mutated)
    arr = [3, 1, 2]
    expected = [1, 2, 3]
    sorter(arr) # 10.2μs -> 10.3μs (1.61% slower)

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

def test_sorter_large_random_ints():
    # Large list of random integers
    arr = [random.randint(-100000, 100000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 29.7ms -> 18.3ms (62.5% faster)

def test_sorter_large_sorted_reverse():
    # Large list sorted in reverse
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 33.1ms -> 23.0ms (44.1% faster)

def test_sorter_large_all_equal():
    # Large list with all elements equal
    arr = [42] * 1000
    expected = [42] * 1000
    codeflash_output = sorter(arr.copy()) # 19.6ms -> 55.7μs (35156% 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()) # 31.6ms -> 19.3ms (64.1% 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()) # 27.3ms -> 15.8ms (72.2% faster)

def test_sorter_large_already_sorted():
    # Large list already sorted
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 20.2ms -> 57.1μs (35212% faster)

def test_sorter_large_alternating():
    # Large list with alternating high/low values
    arr = [i if i % 2 == 0 else 999 - i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 27.3ms -> 16.7ms (63.3% 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 non-integer 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_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 10.3μs (8.50% 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 # 11.4μs -> 10.8μs (6.20% faster)

def test_sorter_unsorted_list():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.5μs (2.77% faster)

def test_sorter_list_with_duplicates():
    # List with duplicate values
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 10.7μs (0.000% faster)

def test_sorter_list_with_negative_numbers():
    # List with negative and positive numbers
    arr = [-1, -3, 2, 0, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 10.8μs (1.55% faster)

def test_sorter_list_with_single_element():
    # Single element list should return the same list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.79μs -> 9.88μs (0.851% slower)

def test_sorter_list_with_two_elements():
    # Two elements, out of order
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 9.92μs (2.10% slower)

def test_sorter_list_with_all_equal_elements():
    # All elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.33μs -> 10.1μs (7.44% slower)

def test_sorter_list_with_floats():
    # List with float numbers
    arr = [3.1, 2.2, 5.5, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.8μs -> 12.3μs (3.72% faster)

def test_sorter_list_with_ints_and_floats():
    # List with ints and floats
    arr = [1, 3.5, 2, 2.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 10.8μs (4.24% faster)

# 2. EDGE TEST CASES

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.46μs -> 9.46μs (0.011% faster)

def test_sorter_list_with_large_numbers():
    # List with very large numbers
    arr = [sys.maxsize, -sys.maxsize, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.4μs -> 11.8μs (4.94% faster)

def test_sorter_list_with_min_max_int():
    # List with min and max integer values
    arr = [0, -2147483648, 2147483647]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 10.7μs (0.775% slower)

def test_sorter_list_with_strings():
    # List with strings (alphabetical order)
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 10.5μs (1.99% faster)

def test_sorter_list_with_empty_strings():
    # List with empty strings
    arr = ['', 'a', '']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 10.5μs (1.60% faster)

def test_sorter_list_with_mixed_case_strings():
    # List with mixed case strings (ASCII order)
    arr = ['a', 'B', 'c', 'A', 'b', 'C']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.6μs -> 11.3μs (2.95% faster)

def test_sorter_list_with_unicode_strings():
    # List with unicode strings
    arr = ['éclair', 'apple', 'Éclair', 'banana']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.3μs -> 12.0μs (3.14% faster)

def test_sorter_list_with_booleans():
    # List with booleans (False < True)
    arr = [True, False, True]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 10.2μs (0.410% slower)

def test_sorter_list_with_none_raises_typeerror():
    # List with None and ints should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.8μs -> 45.5μs (3.75% slower)

def test_sorter_list_with_incomparable_types_raises_typeerror():
    # List with incomparable types should raise TypeError
    arr = [1, 'a', 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 42.4μs -> 44.2μs (4.05% slower)

def test_sorter_list_is_sorted_inplace():
    # Ensure that the original list is sorted in-place and returned
    arr = [4, 3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 10.7μs -> 10.8μs (0.772% slower)

def test_sorter_list_with_large_identical_elements():
    # Large list with all identical elements
    arr = [9] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.7ms -> 55.0μs (35650% faster)

def test_sorter_list_with_one_none_and_all_equal():
    # List with all equal elements except one None (should raise)
    arr = [5, 5, 5, None]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 45.2μs -> 43.5μs (4.02% faster)

# 3. LARGE SCALE TEST CASES

def test_sorter_large_random_int_list():
    # Large random list of integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.2ms -> 18.2ms (65.8% faster)

def test_sorter_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.2ms -> 56.3μs (35706% faster)

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 # 33.2ms -> 23.2ms (43.4% faster)

def test_sorter_large_random_float_list():
    # Large random list of floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.1ms -> 17.5ms (65.9% faster)

def test_sorter_large_random_string_list():
    # Large random list of 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 -> 19.4ms (64.0% faster)

def test_sorter_large_mixed_sign_numbers():
    # Large list with both positive and negative numbers
    arr = [random.choice([-1, 1]) * random.randint(0, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.6ms -> 17.6ms (62.5% faster)

def test_sorter_large_list_with_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 # 27.4ms -> 15.5ms (76.6% 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-md58q9gc and push.