⚡️ Speed up function sorter by 170,977%

[codeflash-ai]

📄 170,977% (1,709.77x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.82 seconds → 2.23 milliseconds (best of 124 runs)

📝 Explanation and details

Here is an optimized version of your code.
The original implementation is a naive bubble sort with O(n²) time complexity and does unnecessary repeated passes.
We can use the built-in list.sort() for in-place sorting, which is highly optimized (Timsort).
This will have the same effect but be much faster and more memory-efficient.

All existing comments are preserved because none existed that warranted a change. The function signature and side effects (printing, returning) remain exactly the same.

✅ 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 Details

• benchmarks/test_benchmark_bubble_sort.py

  • test_sort2: 7.58ms $\textcolor{green}{\rightarrow}$ 22.1μs

• test_bubble_sort.py

  • test_sort: 943ms $\textcolor{green}{\rightarrow}$ 161μs

• test_bubble_sort_conditional.py

  • test_sort: 11.9μs $\textcolor{red}{\rightarrow}$ 8.75μs

• test_bubble_sort_import.py

  • test_sort: 957ms $\textcolor{green}{\rightarrow}$ 160μs

• test_bubble_sort_in_class.py

  • TestSorter.test_sort_in_pytest_class: 937ms $\textcolor{green}{\rightarrow}$ 161μs

• test_bubble_sort_parametrized.py

  • test_sort_parametrized: 572ms $\textcolor{green}{\rightarrow}$ 159μs

• test_bubble_sort_parametrized_loop.py

  • test_sort_loop_parametrized: 136μs $\textcolor{red}{\rightarrow}$ 52.4μs

🌀 Generated Regression Tests Details

import random  # used for generating large-scale test data
import string  # used for string sorting tests
import sys  # used for edge cases 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_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.5μs -> 8.62μs

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.9μs -> 8.50μs

def test_sorter_basic_unsorted():
    # Unsorted list with positive integers
    arr = [3, 1, 4, 2, 5]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.8μs -> 8.33μs

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [4, 2, 2, 3, 1, 4]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.7μs -> 8.54μs

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.9μs -> 8.79μs

def test_sorter_basic_floats():
    # List with floating point numbers
    arr = [3.1, 2.4, 5.6, 1.0, 4.4]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 12.1μs -> 9.62μs

def test_sorter_basic_mixed_int_float():
    # List with both int and float
    arr = [2, 3.5, 1, 4.0, 0]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 12.1μs -> 8.79μs

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 9.83μs -> 8.42μs

def test_sorter_basic_two_elements():
    # List with two elements
    arr = [2, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.1μs -> 8.46μs

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.0μs -> 8.42μs

def test_sorter_basic_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr[:]); result = codeflash_output # 9.54μs -> 8.42μs


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

def test_sorter_edge_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.7μs -> 8.33μs

def test_sorter_edge_alternating():
    # Alternating high/low values
    arr = [1, 100, 1, 100, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 11.0μs -> 8.33μs

def test_sorter_edge_large_numbers():
    # Very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 12.5μs -> 8.67μs

def test_sorter_edge_large_floats():
    # Very large and very small floats
    arr = [1e308, -1e308, 0.0, 1e-308, -1e-308]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 15.6μs -> 11.8μs

def test_sorter_edge_mixed_types():
    # List with int, float, and bool
    arr = [True, 3, 2.2, False, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 12.9μs -> 9.75μs

def test_sorter_edge_strings_case():
    # List of strings with different cases
    arr = ["banana", "Apple", "cherry"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.7μs -> 8.54μs

def test_sorter_edge_unicode_strings():
    # Unicode strings
    arr = ["ápple", "apple", "äpple"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 11.1μs -> 9.46μs

def test_sorter_edge_mutation():
    # Ensure in-place mutation does not affect input when using a copy
    arr = [3, 2, 1]
    arr_copy = arr[:]
    sorter(arr_copy)

def test_sorter_edge_in_place():
    # Ensure in-place mutation does affect the input list
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 10.4μs -> 8.29μs

def test_sorter_edge_non_comparable():
    # List with non-comparable types should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr[:])

def test_sorter_edge_none_element():
    # List with None and other elements should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr[:])

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

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr[:]); result = codeflash_output # 20.6ms -> 36.2μs

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr[:]); result = codeflash_output # 33.7ms -> 36.7μs

def test_sorter_large_random():
    # Large random list
    arr = list(range(1000))
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 29.4ms -> 66.4μs

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[:]); result = codeflash_output # 27.0ms -> 57.4μs

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[:]); result = codeflash_output # 32.5ms -> 98.3μs

def test_sorter_large_negative_positive():
    # Large list with negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 30.6ms -> 71.1μs

def test_sorter_large_alternating():
    # Large alternating pattern
    arr = [i if i % 2 == 0 else 1000 - i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 28.0ms -> 58.4μs

def test_sorter_large_all_equal():
    # Large list of all the same value
    arr = [7] * 1000
    codeflash_output = sorter(arr[:]); result = codeflash_output # 20.3ms -> 33.1μs
# 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 test cases
import sys  # used for testing with very 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_basic_sorted():
    # List is already sorted
    arr = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.62μs

def test_sorter_basic_reverse():
    # List is in reverse order
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 8.50μs

def test_sorter_basic_unsorted():
    # List is unsorted
    arr = [3, 1, 4, 5, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.33μs

def test_sorter_basic_duplicates():
    # List contains duplicates
    arr = [3, 1, 2, 3, 2]
    expected = [1, 2, 2, 3, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.54μs

def test_sorter_basic_negative_numbers():
    # List contains negative numbers
    arr = [0, -1, 3, -2, 2]
    expected = [-2, -1, 0, 2, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 8.79μs

def test_sorter_basic_floats():
    # List contains floats
    arr = [3.1, 2.5, -1.0, 0.0, 2.5]
    expected = [-1.0, 0.0, 2.5, 2.5, 3.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 9.62μs

def test_sorter_basic_single_element():
    # List contains a single element
    arr = [7]
    expected = [7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.83μs -> 8.42μs

def test_sorter_basic_two_elements():
    # List contains two elements
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.46μs

def test_sorter_basic_all_equal():
    # All elements are equal
    arr = [5, 5, 5, 5]
    expected = [5, 5, 5, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 8.42μs

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 8.46μs

def test_sorter_edge_large_numbers():
    # List with very large integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    expected = [-sys.maxsize-1, -999999999, 0, 999999999, sys.maxsize]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.5μs -> 8.67μs

def test_sorter_edge_small_floats():
    # List with very small floats
    arr = [1e-10, -1e-10, 0.0, 1e-20]
    expected = [-1e-10, 0.0, 1e-20, 1e-10]
    # Note: 0.0 < 1e-20 < 1e-10
    # But actually, 0.0 < 1e-20 is False, because 1e-20 > 0.0
    # So the correct sorted order is [-1e-10, 0.0, 1e-20, 1e-10]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.5μs -> 9.38μs

def test_sorter_edge_strings():
    # List of strings (should sort lexicographically)
    arr = ['banana', 'apple', 'cherry', 'date']
    expected = ['apple', 'banana', 'cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 9.04μs

def test_sorter_edge_mixed_types():
    # List with mixed types should raise TypeError
    arr = [1, 'a', 3.0]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_unicode_strings():
    # List with unicode strings
    arr = ['éclair', 'apple', 'Éclair', 'banana']
    expected = ['Éclair', 'apple', 'banana', 'éclair']  # Python default sort is unicode code point order
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 9.46μs

def test_sorter_edge_case_sensitive_strings():
    # List with case-sensitive strings
    arr = ['a', 'A', 'b', 'B']
    expected = ['A', 'B', 'a', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 8.79μs

def test_sorter_edge_nan():
    # List with NaN values (float('nan'))
    arr = [3, float('nan'), 2]
    # Any comparison with NaN is always False, so the sort may not move NaN
    # The result will depend on the sort implementation, but for bubble sort, NaN will "bubble" to the end
    expected = [2, 3, float('nan')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 8.75μs

def test_sorter_edge_infinity():
    # List with infinity values
    arr = [float('inf'), 1, float('-inf'), 0]
    expected = [float('-inf'), 0, 1, float('inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 9.17μs

def test_sorter_edge_mutation():
    # Ensure the function mutates the input list (as per implementation)
    arr = [3, 2, 1]
    expected = [1, 2, 3]
    sorter(arr)

# ---------------------------
# 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 # 30.5ms -> 69.0μs

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 # 27.0ms -> 57.4μs

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.6ms -> 36.2μs

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(1000, 0, -1))
    expected = list(range(1, 1001))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.1ms -> 36.5μs

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 # 32.5ms -> 98.3μs

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 # 29.8ms -> 296μs
# 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-mc0114sh and push.