⚡️ Speed up function sorter by 172,314%

[codeflash-ai]

📄 172,314% (1,723.14x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.38 seconds → 1.96 milliseconds (best of 525 runs)

📝 Explanation and details

Here is a highly optimized version of your sorter function.
Since your bubble sort had nested loops and was O(n²), I've replaced it with Python's built-in sort which is highly tuned (Timsort, O(n log n) in practice), making the function much, much faster and using far less CPU time and memory.

All comments remain as in your sample (none reference bubble sort specifically).
Function signature and return value: exactly the same.

This will be orders of magnitude faster than the original, especially for larger lists.
If there is a requirement to preserve the original arr, you can use sorted_arr = sorted(arr) and return sorted_arr, but your previous code sorts in place, so the above is a perfect drop-in.

Note:
If you must preserve the original O(n²) algorithm (bubble sort), it's possible to optimize your specific loop accordingly with early exit and limiting redundant comparisons; let me know if that's required! Otherwise, the above is the most effective answer for speed.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 62 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details and Performance Breakdown

• benchmarks/test_benchmark_bubble_sort.py

  • test_sort2: 6.88ms -> 16.5μs $\color{green}(+414.79\%)$

• test_bubble_sort.py

  • test_sort: 824ms -> 142μs$\color{green}(+5781.37%)$

• test_bubble_sort_conditional.py

  • test_sort: 5.42μs -> 3.12μs$\color{green}(+0.73%)$

• test_bubble_sort_import.py

  • test_sort: 823ms -> 140μs$\color{green}(+5866.59%)$

• test_bubble_sort_in_class.py

  • TestSorter.test_sort_in_pytest_class: 828ms -> 142μs$\color{green}(+5825.06%)$

• test_bubble_sort_parametrized.py

  • test_sort_parametrized: 501ms -> 143μs$\color{green}(+3501.67%)$

• test_bubble_sort_parametrized_loop.py

  • test_sort_loop_parametrized: 102μs -> 20.7μs$\color{green}(+3.94%)$

🌀 Generated Regression Tests Details and Performance Breakdown

import random  # used for large scale random input generation
import string  # used for string sorting tests
import sys  # used for edge case with large/small numbers

# 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 # 3.67μs -> 2.79μs

def test_sorter_single_element():
    # Test sorting a single-element list
    arr = [5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.92μs

def test_sorter_already_sorted():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.42μs -> 2.96μs

def test_sorter_reverse_sorted():
    # Test sorting a reverse-sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 2.96μs

def test_sorter_unsorted():
    # Test sorting a typical unsorted list
    arr = [3, 1, 4, 2, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 2.92μs

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate values
    arr = [4, 2, 2, 5, 3, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 3.08μs

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [0, -1, 5, -10, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 3.08μs

def test_sorter_all_equal():
    # Test sorting a list where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.21μs -> 2.92μs

def test_sorter_floats():
    # Test sorting a list of floats
    arr = [3.2, 1.5, 4.8, 2.3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 3.50μs

def test_sorter_mixed_int_float():
    # Test sorting a list of mixed ints and floats
    arr = [2, 3.5, 1, 2.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.33μs -> 3.54μs

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.12μs

def test_sorter_strings_case_sensitive():
    # Test sorting a list of strings with different cases
    arr = ["banana", "Apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 3.00μs

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

def test_sorter_large_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 -> 3.54μs

def test_sorter_nearly_sorted():
    # Test sorting a list that is nearly sorted except for one element
    arr = [1, 2, 3, 5, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 2.96μs

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

def test_sorter_with_unorderable_types_raises():
    # Test that sorting a list with unorderable types raises TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_nested_lists_raises():
    # Test that sorting a list with nested lists raises TypeError
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

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

def test_sorter_sorting_empty_strings():
    # Test sorting a list of empty strings
    arr = ["", "", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.29μs -> 3.00μs

def test_sorter_sorting_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.71μs -> 3.50μs

def test_sorter_large_negative_floats():
    # Test sorting with large negative floats
    arr = [-1e308, 1e308, 0.0, -1e-308]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.29μs -> 4.71μs

def test_sorter_mutable_elements_raises():
    # Test that sorting a list with unorderable mutable elements raises TypeError
    arr = [{}, {"a": 1}]
    with pytest.raises(TypeError):
        sorter(arr.copy())

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

def test_sorter_large_random_ints():
    # Test sorting a large list of random integers
    arr = random.sample(range(-100000, -99000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.7ms -> 61.7μs

def test_sorter_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.3ms -> 29.0μs

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 # 30.3ms -> 29.2μs

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 -> 49.7μs

def test_sorter_large_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 # 29.9ms -> 89.1μs

def test_sorter_large_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 # 26.1ms -> 288μs

def test_sorter_large_nearly_sorted():
    # Test sorting a large nearly sorted list (one swap at the end)
    arr = list(range(1000))
    arr[-1], arr[-2] = arr[-2], arr[-1]
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.6ms -> 29.3μs

def test_sorter_large_all_equal():
    # Test sorting a large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.1ms -> 27.5μs
# 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

# 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_basic_sorted():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.04μs

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 2.96μs

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 2.96μs

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [4, 2, 2, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 2.92μs

def test_sorter_basic_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 2.92μs

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, -5, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.17μs

def test_sorter_basic_mixed_signs():
    # List with both positive and negative numbers
    arr = [3, -2, 0, 1, -5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.04μs

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.79μs

def test_sorter_basic_two_elements_sorted():
    # Two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 2.88μs

def test_sorter_basic_two_elements_unsorted():
    # Two elements unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 2.83μs

# 2. Edge Test Cases

def test_sorter_edge_empty():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.38μs -> 2.75μs

def test_sorter_edge_large_numbers():
    # List with very large and very small integers
    arr = [2**31-1, -2**31, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.33μs -> 3.42μs

def test_sorter_edge_floats():
    # List with floats
    arr = [3.1, 2.2, 5.5, 1.0, 4.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 3.71μs

def test_sorter_edge_negative_floats():
    # List with negative floats
    arr = [-1.1, -3.3, -2.2, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.46μs

def test_sorter_edge_mixed_int_float():
    # List with mixed integers and floats
    arr = [1, 2.2, 3, 0.5, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.62μs -> 3.62μs

def test_sorter_edge_strings():
    # List of strings (alphabetical sort)
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.21μs

def test_sorter_edge_strings_case():
    # List of strings with mixed case (ASCII order)
    arr = ["Banana", "apple", "Apple", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.17μs

def test_sorter_edge_single_char_strings():
    # List of single character strings
    arr = ['z', 'a', 'd', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.04μs

def test_sorter_edge_unicode_strings():
    # List with unicode strings
    arr = ['é', 'a', 'ü', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 3.25μs

def test_sorter_edge_all_equal_strings():
    # All strings are the same
    arr = ['same', 'same', 'same']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.17μs -> 3.04μs

def test_sorter_edge_custom_objects():
    # Sorting custom objects should raise TypeError
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_incomparable_types():
    # List with incomparable types should raise TypeError
    arr = [1, "string"]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_nan():
    # List with float('nan') values
    arr = [3, float('nan'), 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.38μs

def test_sorter_edge_inf():
    # List with float('inf') and float('-inf')
    arr = [3, float('inf'), 2, float('-inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.50μs -> 3.29μs

# 3. 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.copy()); result = codeflash_output # 28.2ms -> 60.1μs

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 # 24.4ms -> 48.4μs

def test_sorter_large_sorted():
    # Large list already sorted
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.4ms -> 28.8μs

def test_sorter_large_reverse_sorted():
    # Large list sorted in reverse
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.4ms -> 28.8μs

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 # 29.6ms -> 89.5μs

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.4ms -> 282μs

def test_sorter_large_all_equal():
    # Large list with all elements equal
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.2ms -> 27.6μs

def test_sorter_large_alternating():
    # Large list alternating between two numbers
    arr = [1, 0] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 21.5ms -> 43.5μs
# 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-mc13b2zx and push.