⚡️ Speed up function sorter by 217,337%

[codeflash-ai]

📄 217,337% (2,173.37x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.34 seconds → 1.53 milliseconds (best of 510 runs)

📝 Explanation and details

Here is an optimized version of your program. The original uses a naive bubble sort (O(n^2)); using Timsort (the built-in sorted or list.sort(), which is O(n log n)) is much faster for all but the smallest lists. I've preserved output, function signature, and return value.

This is much faster and uses less memory than making a new list or reimplementing a sort.

✅ 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 Tests Details

• benchmarks/test_benchmark_bubble_sort.py

  • test_sort2: $${\color{red}6.84ms \rightarrow 16.7μs}$$

• test_bubble_sort.py

  • test_sort: $${\color{green}838ms \rightarrow 138μs}$$

• test_bubble_sort_conditional.py

  • test_sort: 6.25μs -> 3.08μs

• test_bubble_sort_import.py

  • test_sort: 833ms -> 140μs

• test_bubble_sort_in_class.py

  • TestSorter.test_sort_in_pytest_class: 833ms -> 139μs

• test_bubble_sort_parametrized.py

  • test_sort_parametrized: 513ms -> 139μs

• test_bubble_sort_parametrized_loop.py

  • test_sort_loop_parametrized: 106μs -> 21.1μs

• test_sorter__unit_test_0.py

  • test_all_equal_elements: 4.38μs -> 2.96μs
  • test_already_sorted_with_duplicates: 5.04μs -> 3.08μs
  • test_duplicates: 5.04μs -> 3.12μs
  • test_empty_list: 3.96μs -> 2.88μs
  • test_floats_and_ints: 6.50μs -> 3.88μs
  • test_large_list_all_equal: 18.5ms -> 26.6μs
  • test_large_list_with_duplicates: 25.2ms -> 52.0μs
  • test_large_list_with_negatives_and_positives: 29.3ms -> 63.9μs
  • test_large_negative_and_positive: 6.42μs -> 3.58μs
  • test_large_negative_float: 7.54μs -> 4.75μs
  • test_large_random_list: 29.1ms -> 63.0μs
  • test_large_reverse_sorted_list: 30.8ms -> 29.4μs
  • test_large_sorted_list: 18.5ms -> 29.9μs
  • test_list_with_min_max_int: 4.17μs -> 3.12μs
  • test_list_with_zeros: 4.67μs -> 3.12μs
  • test_mixed_positive_negative: 4.46μs -> 3.04μs
  • test_mutation_of_input: 3.92μs -> 3.00μs
  • test_nan_values: 4.54μs -> 3.46μs
  • test_negative_numbers: 4.58μs -> 3.08μs
  • test_reverse_sorted_list: 5.17μs -> 3.08μs
  • test_single_element: 4.08μs -> 3.04μs
  • test_sorted_list: 4.46μs -> 3.12μs
  • test_stability_with_duplicates: 4.25μs -> 3.04μs
  • test_two_elements_sorted: 3.33μs -> 3.00μs
  • test_two_elements_unsorted: 3.50μs -> 2.96μs
  • test_unsorted_list: 5.17μs -> 3.17μs

• test_sorter__unit_test_1.py

  • test_sorter_all_identical_elements: 4.38μs -> 3.00μs
  • test_sorter_empty_list: 3.58μs -> 2.83μs
  • test_sorter_empty_strings: 4.29μs -> 3.17μs
  • test_sorter_floats: 6.12μs -> 3.54μs
  • test_sorter_inplace_behavior: 4.96μs -> 3.04μs
  • test_sorter_large_and_small_integers: 6.54μs -> 3.62μs
  • test_sorter_large_float_list: 27.4ms -> 278μs
  • test_sorter_large_list_with_duplicates: 25.3ms -> 49.2μs
  • test_sorter_large_random_list: 28.2ms -> 63.8μs
  • test_sorter_large_reverse_sorted_list: 30.7ms -> 30.0μs
  • test_sorter_large_sorted_list: 18.5ms -> 29.7μs
  • test_sorter_large_string_list: 30.3ms -> 93.4μs
  • test_sorter_list_with_duplicates: 5.21μs -> 3.04μs
  • test_sorter_mixed_int_and_float: 5.50μs -> 3.62μs
  • test_sorter_mixed_types: 2.54μs -> 2.04μs
  • test_sorter_negative_numbers: 4.79μs -> 3.08μs
  • test_sorter_negative_zero: 4.75μs -> 3.38μs
  • test_sorter_nested_lists: 3.21μs -> 1.92μs
  • test_sorter_object_elements: 2.54μs -> 1.96μs
  • test_sorter_reverse_sorted_list: 5.21μs -> 3.08μs
  • test_sorter_single_element: 3.92μs -> 2.92μs
  • test_sorter_sorted_list: 4.79μs -> 3.08μs
  • test_sorter_strings: 4.67μs -> 3.38μs
  • test_sorter_strings_with_case: 4.08μs -> 3.29μs
  • test_sorter_two_elements_sorted: 4.04μs -> 2.88μs
  • test_sorter_two_elements_unsorted: 4.04μs -> 2.96μs
  • test_sorter_unsorted_list: 4.96μs -> 3.12μs

🌀 Generated Regression Tests Details

import random
import sys

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort import sorter

# unit tests

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

def test_empty_list():
    # Test that an empty list returns an empty list
    codeflash_output = sorter([]) # 3.96μs -> 2.88μs

def test_single_element():
    # Test that a single-element list is unchanged
    codeflash_output = sorter([42]) # 4.08μs -> 3.04μs

def test_sorted_list():
    # Test that a sorted list remains unchanged
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 4.46μs -> 3.12μs

def test_reverse_sorted_list():
    # Test that a reverse-sorted list is sorted correctly
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 5.17μs -> 3.08μs

def test_unsorted_list():
    # Test a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 1, 5, 9, 2]) # 5.17μs -> 3.17μs

def test_duplicates():
    # Test a list with duplicate elements
    codeflash_output = sorter([2, 3, 2, 1, 3, 1]) # 5.04μs -> 3.12μs

def test_negative_numbers():
    # Test a list with negative numbers
    codeflash_output = sorter([-3, -1, -2, -5, 0]) # 4.58μs -> 3.08μs

def test_mixed_positive_negative():
    # Test a list with both positive and negative numbers
    codeflash_output = sorter([-2, 3, 1, -1, 0]) # 4.46μs -> 3.04μs

def test_all_equal_elements():
    # Test a list where all elements are equal
    codeflash_output = sorter([7, 7, 7, 7]) # 4.38μs -> 2.96μs

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

def test_large_negative_and_positive():
    # Test with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999, -999999]
    expected = [-sys.maxsize-1, -999999, 0, 999999, sys.maxsize]
    codeflash_output = sorter(arr) # 6.42μs -> 3.58μs

def test_already_sorted_with_duplicates():
    # Test a sorted list with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    expected = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr) # 5.04μs -> 3.08μs

def test_two_elements_sorted():
    # Test two elements already sorted
    codeflash_output = sorter([1, 2]) # 3.33μs -> 3.00μs

def test_two_elements_unsorted():
    # Test two elements unsorted
    codeflash_output = sorter([2, 1]) # 3.50μs -> 2.96μs

def test_list_with_zeros():
    # Test a list with zeros and positive/negative numbers
    codeflash_output = sorter([0, -1, 1, 0, -2, 2]) # 4.67μs -> 3.12μs

def test_list_with_min_max_int():
    # Test with min and max 32-bit integers
    arr = [2147483647, -2147483648, 0]
    expected = [-2147483648, 0, 2147483647]
    codeflash_output = sorter(arr) # 4.17μs -> 3.12μs

def test_stability_with_duplicates():
    # Test stability: relative order of equal elements (not guaranteed for this bubble sort, but check anyway)
    arr = [3, 1, 2, 1, 3]
    expected = [1, 1, 2, 3, 3]
    codeflash_output = sorter(arr) # 4.25μs -> 3.04μs

def test_floats_and_ints():
    # Test a list with both floats and ints
    arr = [3.2, 1, 2.5, 1.0, 3]
    expected = [1, 1.0, 2.5, 3, 3.2]
    codeflash_output = sorter(arr) # 6.50μs -> 3.88μs

def test_large_negative_float():
    # Test with large negative and positive floats
    arr = [float('-inf'), -1e308, 0.0, 1e308, float('inf')]
    expected = [float('-inf'), -1e308, 0.0, 1e308, float('inf')]
    codeflash_output = sorter(arr) # 7.54μs -> 4.75μs

def test_nan_values():
    # Test with NaN values (should be at the end after sorting in Python's default sort)
    arr = [3, float('nan'), 2, float('nan'), 1]
    codeflash_output = sorter(arr); result = codeflash_output # 4.54μs -> 3.46μs
    # NaNs should be at the end, but bubble sort may not handle this as Python's sort does
    # So verify that all non-NaN values are sorted, and NaNs are present
    non_nan = [x for x in result if x == x]
    nan_count = sum(1 for x in result if x != x)

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

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

def test_large_sorted_list():
    # Test a large already sorted list (performance and correctness)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 18.5ms -> 29.9μs

def test_large_reverse_sorted_list():
    # Test a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()) # 30.8ms -> 29.4μs

def test_large_random_list():
    # Test a large random list
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 29.1ms -> 63.0μs

def test_large_list_with_duplicates():
    # Test 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()) # 25.2ms -> 52.0μs

def test_large_list_all_equal():
    # Test a large list where all elements are the same
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()) # 18.5ms -> 26.6μs

def test_large_list_with_negatives_and_positives():
    # Test a large list with both negative and positive numbers
    arr = [random.randint(-500, 500) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 29.3ms -> 63.9μs
# 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 integer values

# 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 # 4.79μs -> 3.08μs

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.08μs

def test_sorter_unsorted_list():
    # Unsorted list with mixed numbers
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.12μs

def test_sorter_list_with_duplicates():
    # List with duplicate elements should sort correctly
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.04μs

def test_sorter_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.92μs

def test_sorter_two_elements_sorted():
    # Two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 2.88μs

def test_sorter_two_elements_unsorted():
    # Two elements unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 2.96μs

def test_sorter_negative_numbers():
    # List containing negative numbers
    arr = [0, -1, -3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.08μs

def test_sorter_floats():
    # List containing floats
    arr = [2.2, 1.1, 3.3, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 3.54μs

def test_sorter_mixed_int_and_float():
    # List with both ints and floats
    arr = [1, 2.5, 0, 1.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.50μs -> 3.62μs

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

def test_sorter_empty_list():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.58μs -> 2.83μs

def test_sorter_all_identical_elements():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 3.00μs

def test_sorter_large_and_small_integers():
    # 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 # 6.54μs -> 3.62μs

def test_sorter_strings():
    # List of strings should be sorted lexicographically
    arr = ['banana', 'apple', 'cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.38μs

def test_sorter_strings_with_case():
    # Strings with different cases (Python's default is uppercase < lowercase)
    arr = ['Banana', 'apple', 'Cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 3.29μs

def test_sorter_empty_strings():
    # List with empty strings
    arr = ['', 'a', '', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.29μs -> 3.17μs

def test_sorter_negative_zero():
    # List with -0.0 and 0.0 (should be considered equal)
    arr = [0.0, -0.0, 1.0, -1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.38μs

def test_sorter_nested_lists():
    # List with nested lists should raise TypeError in Python 3 (unorderable types)
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_mixed_types():
    # List with mixed types (e.g., int and str) should raise TypeError
    arr = [1, 'two', 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_object_elements():
    # List with objects that cannot be compared
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        sorter(arr.copy())

# --------------------
# 3. Large Scale Test Cases
# --------------------

def test_sorter_large_random_list():
    # Sorting a 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 # 28.2ms -> 63.8μs

def test_sorter_large_sorted_list():
    # Sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 29.7μs

def test_sorter_large_reverse_sorted_list():
    # Sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.7ms -> 30.0μs

def test_sorter_large_list_with_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 # 25.3ms -> 49.2μs

def test_sorter_large_string_list():
    # 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 # 30.3ms -> 93.4μs

def test_sorter_large_float_list():
    # 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 # 27.4ms -> 278μs

# --------------------
# Additional Robustness
# --------------------

def test_sorter_inplace_behavior():
    # Ensure that sorter sorts in-place and returns the same object
    arr = [2, 1, 3]
    codeflash_output = sorter(arr); result = codeflash_output # 4.96μs -> 3.04μs

To edit these changes git checkout codeflash/optimize-sorter-mbzv0dh9 and push.