⚡️ Speed up function sorter by 208,818%

[codeflash-ai]

📄 208,818% (2,088.18x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.33 seconds → 1.59 milliseconds (best of 482 runs)

📝 Explanation and details

Here is an optimized version of your program. The original implements a naive bubble sort, which is very slow (O(n²)). To make it run much faster, replace the sort logic with Python's built-in sort (timsort, O(n log n)). The function signature, return value, and print statements are unchanged.

This is the fastest and most memory-efficient way to sort a list in Python 3.12.10 while preserving existing comments and functionality.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 57 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 File | 🧬Test Name | ⏱️Original | ⏱️Optimized | 💯Gain |
  |:-------------------------------------------|:--------------|:-------------|:--------------|:-----------|
  | benchmarks/test_benchmark_bubble_sort.py | test_sort2 | 7.03ms | 17.0μs | ✅41133.66 |
• test_bubble_sort.py
  | 🧪Test File | 🧬Test Name | ⏱️Original | ⏱️Optimized | 💯Gain |
  |:-------------------------------------------|:--------------|:-------------|:--------------|:------------|
  | benchmarks/test_benchmark_bubble_sort.py | test_sort2 | 7.03ms | 17.0μs | ✅41133.66 |
  | test_bubble_sort.py | test_sort | 832ms | 140μs | ✅593770.80 |
• test_bubble_sort_conditional.py
  | 🧪Test File | 🧬Test Name | ⏱️Original | ⏱️Optimized | 💯Gain |
  |:-------------------------------------------|:--------------|:-------------|:--------------|:------------|
  | benchmarks/test_benchmark_bubble_sort.py | test_sort2 | 7.03ms | 17.0μs | ✅41133.66 |
  | test_bubble_sort.py | test_sort | 832ms | 140μs | ✅593770.80 |
  | test_bubble_sort_conditional.py | test_sort | 6.83μs | 3.08μs | ✅121.67 |
• test_bubble_sort_import.py
  | 🧪Test File | 🧬Test Name | ⏱️Original | ⏱️Optimized | 💯Gain |
  |:-------------------------------------------|:--------------|:-------------|:--------------|:------------|
  | benchmarks/test_benchmark_bubble_sort.py | test_sort2 | 7.03ms | 17.0μs | ✅41133.66 |
  | test_bubble_sort.py | test_sort | 832ms | 140μs | ✅593770.80 |
  | test_bubble_sort_conditional.py | test_sort | 6.83μs | 3.08μs | ✅121.67 |
  | test_bubble_sort_import.py | test_sort | 832ms | 137μs | ✅603704.09 |
• test_bubble_sort_in_class.py
  | 🧪Test File | 🧬Test Name | ⏱️Original | ⏱️Optimized | 💯Gain |
  |:-------------------------------------------|:---------------------------------------|:-------------|:--------------|:------------|
  | benchmarks/test_benchmark_bubble_sort.py | test_sort2 | 7.03ms | 17.0μs | ✅41133.66 |
  | test_bubble_sort.py | test_sort | 832ms | 140μs | ✅593770.80 |
  | test_bubble_sort_conditional.py | test_sort | 6.83μs | 3.08μs | ✅121.67 |
  | test_bubble_sort_import.py | test_sort | 832ms | 137μs | ✅603704.09 |
  | test_bubble_sort_in_class.py | TestSorter.test_sort_in_pytest_class | 837ms | 139μs | ✅600221.76 |
• test_bubble_sort_parametrized.py
  | 🧪Test File | 🧬Test Name | ⏱️Original | ⏱️Optimized | 💯Gain |
  |:-------------------------------------------|:---------------------------------------|:-------------|:--------------|:------------|
  | benchmarks/test_benchmark_bubble_sort.py | test_sort2 | 7.03ms | 17.0μs | ✅41133.66 |
  | test_bubble_sort.py | test_sort | 832ms | 140μs | ✅593770.80 |
  | test_bubble_sort_conditional.py | test_sort | 6.83μs | 3.08μs | ✅121.67 |
  | test_bubble_sort_import.py | test_sort | 832ms | 137μs | ✅603704.09 |
  | test_bubble_sort_in_class.py | TestSorter.test_sort_in_pytest_class | 837ms | 139μs | ✅600221.76 |
  | test_bubble_sort_parametrized.py | test_sort_parametrized | 507ms | 139μs | ✅363354.00 |
• test_bubble_sort_parametrized_loop.py
  | 🧪Test File | 🧬Test Name | ⏱️Original | ⏱️Optimized | 💯Gain |
  |:-------------------------------------------|:---------------------------------------|:-------------|:--------------|:------------|
  | benchmarks/test_benchmark_bubble_sort.py | test_sort2 | 7.03ms | 17.0μs | ✅41133.66 |
  | test_bubble_sort.py | test_sort | 832ms | 140μs | ✅593770.80 |
  | test_bubble_sort_conditional.py | test_sort | 6.83μs | 3.08μs | ✅121.67 |
  | test_bubble_sort_import.py | test_sort | 832ms | 137μs | ✅603704.09 |
  | test_bubble_sort_in_class.py | TestSorter.test_sort_in_pytest_class | 837ms | 139μs | ✅600221.76 |
  | test_bubble_sort_parametrized.py | test_sort_parametrized | 507ms | 139μs | ✅363354.00 |
  | test_bubble_sort_parametrized_loop.py | test_sort_loop_parametrized | 105μs | 21.8μs | ✅383.57 |

🌀 Generated Regression Tests Details and Performance Breakdown

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for edge value tests

# 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 # 3.88μs -> 3.00μs (29.17%)

def test_sorter_single_element():
    # Test sorting a list with one element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.96μs (32.38%)

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 # 4.71μs -> 3.04μs (54.77%)

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 # 5.21μs -> 3.04μs (71.29%)

def test_sorter_unsorted_integers():
    # Test sorting a typical unsorted list
    arr = [3, 1, 4, 1, 5, 9, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 3.21μs (74.03%)

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate elements
    arr = [4, 2, 2, 8, 3, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.79μs -> 3.17μs (82.94%)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [0, -1, -3, 2, -2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.25μs -> 3.17μs (65.77%)

def test_sorter_floats():
    # Test sorting a list with floats
    arr = [3.1, 2.4, -1.5, 0.0, 2.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.25μs -> 3.83μs (63.06%)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 3.33μs (31.26%)

def test_sorter_mixed_case_strings():
    # Test sorting a list of strings with mixed cases
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.25μs (41.02%)


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

def test_sorter_all_identical_elements():
    # Test sorting a list where all elements are the same
    arr = [7] * 10
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 3.17μs (92.07%)

def test_sorter_two_elements_sorted():
    # Test sorting a two-element list already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.96μs (32.39%)

def test_sorter_two_elements_unsorted():
    # Test sorting a two-element list unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.96μs (31.00%)

def test_sorter_min_max_integers():
    # Test sorting a list with min and max integer values
    arr = [sys.maxsize, -sys.maxsize - 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.33μs (50.02%)

def test_sorter_min_max_floats():
    # Test sorting a list with min and max float values
    arr = [float('-inf'), float('inf'), 0.0, -1.0, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.96μs -> 3.50μs (70.26%)

def test_sorter_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 3.79μs (60.42%)

def test_sorter_empty_strings():
    # Test sorting a list with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.21μs (53.27%)

def test_sorter_large_negative_and_positive():
    # Test sorting a list with large negative and positive numbers
    arr = [-999999999, 999999999, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.25μs (42.31%)

def test_sorter_stability():
    # Test sorting stability (should preserve order of equal elements)
    class StableObj:
        def __init__(self, key, id):
            self.key = key
            self.id = id
        def __lt__(self, other):
            return self.key < other.key
        def __eq__(self, other):
            return self.key == other.key and self.id == other.id
        def __repr__(self):
            return f"({self.key},{self.id})"
    arr = [StableObj(1, 'a'), StableObj(1, 'b'), StableObj(0, 'c')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.96μs -> 4.25μs (63.72%)

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

def test_sorter_non_comparable_elements():
    # Test sorting a list with non-comparable elements should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())


def test_sorter_none_elements():
    # Test sorting a list with None values (should raise TypeError)
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_large_string_elements():
    # Test sorting a list with very large strings
    arr = ["a" * 1000, "b" * 999, "a" * 1001]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 9.25μs (13.51%)


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

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers (~1000 elements)
    arr = [random.randint(-100000, 100000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.4ms -> 67.2μs (42111.59%)

def test_sorter_large_sorted_input():
    # Test sorting a large already-sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.7ms -> 29.8μs (62512.29%)

def test_sorter_large_reverse_sorted_input():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.8ms -> 29.0μs (106279.21%)

def test_sorter_large_duplicates():
    # Test sorting a large list with many duplicates
    arr = [random.choice([0, 1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 25.2ms -> 55.7μs (45137.44%)

def test_sorter_large_strings():
    # Test sorting a large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=10)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.7ms -> 106μs (27709.37%)

def test_sorter_large_negative_and_positive_floats():
    # Test sorting a large list of random floats between -1e6 and 1e6
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 26.2ms -> 287μs (9018.79%)
# 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 large scale random data

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

# unit tests

# ----------------------- 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 # 5.75μs -> 3.17μs (81.56%)

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 # 5.46μs -> 3.08μs (77.01%)

def test_sorter_unsorted_list():
    # Unsorted list with mixed values
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.04μs (65.71%)

def test_sorter_with_duplicates():
    # List with duplicate values
    arr = [4, 2, 2, 3, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.08μs (51.35%)

def test_sorter_single_element():
    # List with a single element should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 3.04μs (31.54%)

def test_sorter_two_elements_sorted():
    # Two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 2.92μs (37.13%)

def test_sorter_two_elements_unsorted():
    # Two elements unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 2.88μs (42.05%)

def test_sorter_all_equal_elements():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.21μs -> 2.96μs (42.29%)

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.12μs (54.69%)

def test_sorter_mixed_positive_negative():
    # List with both positive and negative numbers
    arr = [3, -2, -1, 2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.25μs (47.42%)

def test_sorter_floats():
    # List with floating point numbers
    arr = [3.2, 1.5, 4.8, 2.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.58μs -> 3.83μs (71.75%)

def test_sorter_mixed_ints_and_floats():
    # List with both integers and floats
    arr = [3, 1.1, 2, 4.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 3.71μs (47.22%)

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.88μs (30.43%)

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

def test_sorter_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 # 6.67μs -> 3.71μs (79.80%)

def test_sorter_already_sorted_with_duplicates():
    # Already sorted list with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.08μs (64.90%)

def test_sorter_descending_with_duplicates():
    # Descending order with duplicates
    arr = [5, 4, 4, 3, 2, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 3.12μs (92.00%)

def test_sorter_list_with_zeroes():
    # List with multiple zeroes
    arr = [0, 0, 1, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.08μs (55.38%)

def test_sorter_list_with_min_max_int():
    # List with Python's min and max integer values
    arr = [0, -9223372036854775808, 9223372036854775807]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.25μs (46.15%)

def test_sorter_list_with_nan_inf():
    # List with float('nan'), float('inf'), float('-inf')
    arr = [1, float('nan'), 2, float('inf'), float('-inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.88μs -> 3.79μs (54.93%)

def test_sorter_list_with_only_nan():
    # List with only NaN values
    arr = [float('nan'), float('nan')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 3.21μs (26.00%)

def test_sorter_list_with_only_inf():
    # List with only infinities
    arr = [float('inf'), float('-inf'), float('inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.12μs (46.66%)

def test_sorter_mutation_of_input():
    # Ensure the function mutates the input list (since that's how it's written)
    arr = [2, 1]
    sorter(arr)

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

def test_sorter_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 29.7μs (62036.46%)

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 -> 29.9μs (103012.13%)

def test_sorter_large_random_list():
    # Large list with random values
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.1ms -> 64.4μs (43510.83%)

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 # 24.7ms -> 52.2μs (47131.18%)

def test_sorter_large_list_all_equal():
    # Large list where all elements are the same
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.0ms -> 26.9μs (66706.45%)

def test_sorter_large_list_negative_and_positive():
    # Large list with both negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.2ms -> 64.6μs (43633.45%)
# 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-mc19v0yi and push.