⚡️ Speed up function sorter by 172,212%

[codeflash-ai]

📄 172,212% (1,722.12x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.32 seconds → 1.93 milliseconds (best of 508 runs)

📝 Explanation and details

Here’s an optimized version of your program. Your original code uses a nested loop for bubble sort, which is O(n²). Python’s built-in sort() function (Timsort, O(n log n)) is much faster and more memory-efficient since it sorts in place.

All comments are preserved, and functionality is identical, but the run time will be much faster for all but minuscule lists.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 64 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: 6.98ms $$\rightarrow$$ 16.7μs

• test_bubble_sort.py
  $\colorbox{lime}{ - test_sort: 824ms \rightarrow 140μs}$

• test_bubble_sort_conditional.py
  $\colorbox{lime}{ - test_sort: 7.00μs \rightarrow 3.08μs}$

• test_bubble_sort_import.py
  $\colorbox{lime}{ - test_sort: 821ms \rightarrow 139μs}$

• test_bubble_sort_in_class.py
  $\colorbox{lime}{ - TestSorter.test_sort_in_pytest_class: 819ms \rightarrow 140μs}$

• test_bubble_sort_parametrized.py
  $\colorbox{lime}{ - test_sort_parametrized: 503ms \rightarrow 142μs}$

• test_bubble_sort_parametrized_loop.py
  $\colorbox{lime}{ - test_sort_loop_parametrized: 104μs \rightarrow 20.9μs}$

🌀 Generated Regression Tests Details

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for edge numeric 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_sort_basic_integers():
    # Test sorting a simple list of positive integers
    arr = [3, 1, 4, 1, 5, 9, 2, 6]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 6.33μs -> 3.21μs

def test_sort_already_sorted():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 4.54μs -> 3.00μs

def test_sort_reverse_sorted():
    # Test sorting a reverse-sorted list
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 5.08μs -> 2.96μs

def test_sort_with_duplicates():
    # Test sorting a list with duplicate elements
    arr = [2, 3, 2, 1, 3, 1]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 5.04μs -> 3.00μs

def test_sort_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-3, -1, -4, -2, 0]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 4.75μs -> 3.00μs

def test_sort_mixed_sign_numbers():
    # Test sorting a list with both positive and negative numbers
    arr = [3, -1, 4, -2, 0]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 4.92μs -> 3.00μs

def test_sort_floats():
    # Test sorting a list of floats
    arr = [3.1, 2.4, 5.6, 1.2]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 5.50μs -> 3.46μs

def test_sort_mixed_int_float():
    # Test sorting a list with both ints and floats
    arr = [1, 2.2, 3, 0.5, -1.1]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 6.96μs -> 3.67μs

def test_sort_strings():
    # Test sorting a list of strings alphabetically
    arr = ["banana", "apple", "cherry"]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 4.67μs -> 3.00μs

def test_sort_single_element():
    # Test sorting a list with a single element
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()) # 3.46μs -> 2.92μs

# -------------------------
# 2. EDGE TEST CASES
# -------------------------

def test_sort_empty_list():
    # Test sorting an empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()) # 3.58μs -> 2.75μs

def test_sort_all_identical():
    # Test sorting a list where all elements are the same
    arr = [7] * 10
    expected = [7] * 10
    codeflash_output = sorter(arr.copy()) # 5.71μs -> 3.08μs

def test_sort_two_elements_sorted():
    # Test sorting a two-element list that is already sorted
    arr = [1, 2]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 3.96μs -> 2.83μs

def test_sort_two_elements_unsorted():
    # Test sorting a two-element list that is not sorted
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 3.83μs -> 2.88μs

def test_sort_with_large_numbers():
    # Test sorting a list with very large integers
    arr = [sys.maxsize, 0, -sys.maxsize]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 5.17μs -> 3.12μs

def test_sort_with_small_numbers():
    # Test sorting a list with very small (negative) integers
    arr = [-sys.maxsize - 1, 0, sys.maxsize]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 4.42μs -> 3.08μs

def test_sort_with_nan_inf():
    # Test sorting a list with float('nan'), float('inf'), float('-inf')
    arr = [float('inf'), 1.0, float('-inf'), 0.0]
    # sorted() puts -inf first, then numbers, then inf, nan always last
    arr_with_nan = arr + [float('nan')]
    expected = sorted(arr_with_nan, key=lambda x: (isinstance(x, float) and (x != x), x))
    codeflash_output = sorter(arr_with_nan.copy()); result = codeflash_output # 5.75μs -> 3.42μs

def test_sort_strings_case_sensitive():
    # Test sorting a list of strings with mixed case
    arr = ["apple", "Banana", "cherry", "Apple"]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 5.08μs -> 3.21μs

def test_sort_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ["café", "apple", "banana", "Álgebra"]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 6.00μs -> 3.54μs

def test_sort_list_of_lists():
    # Test sorting a list of lists (lexicographical order)
    arr = [[2, 3], [1, 2], [2, 2], [1, 1]]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 5.67μs -> 3.50μs

def test_sort_custom_objects_raises():
    # Test sorting a list of objects without __lt__ should raise TypeError
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sort_mixed_types_raises():
    # Test sorting a list with incompatible types should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sort_large_and_small_floats():
    # Test sorting a list with very large and very small floats
    arr = [1e308, 1e-308, 0.0, -1e308, -1e-308]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 8.79μs -> 4.88μs

# -------------------------
# 3. LARGE SCALE TEST CASES
# -------------------------

def test_sort_large_random_integers():
    # Test sorting a large list of random integers
    arr = random.sample(range(-1000000, -999000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 28.1ms -> 63.7μs

def test_sort_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 18.7ms -> 29.0μs

def test_sort_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()) # 30.7ms -> 29.4μs

def test_sort_large_strings():
    # Test sorting a large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=8)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 30.1ms -> 100μs

def test_sort_large_duplicates():
    # Test sorting a 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()) # 24.8ms -> 50.8μs

def test_sort_large_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e5, 1e5) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 26.4ms -> 288μs

# -------------------------
# 4. ADDITIONAL EDGE CASES
# -------------------------

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

def test_sort_returns_same_object():
    # Test that the returned list is the same object as input (in-place sort)
    arr = [5, 4, 3]
    codeflash_output = sorter(arr); result = codeflash_output # 4.25μs -> 3.00μs

def test_sort_empty_list_identity():
    # Test that sorting an empty list returns the same object
    arr = []
    codeflash_output = sorter(arr); result = codeflash_output # 3.25μs -> 2.75μs

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

# 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 # 5.38μs -> 3.08μs

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should be sorted ascendingly
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 2.92μs

def test_sorter_unsorted_list():
    # Unsorted list with random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.00μs

def test_sorter_list_with_duplicates():
    # List containing duplicate values
    arr = [2, 3, 2, 1, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 2.96μs

def test_sorter_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 2.79μs

def test_sorter_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.88μs

def test_sorter_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.83μs

def test_sorter_all_elements_equal():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 2.96μs

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.04μs

def test_sorter_mixed_positive_negative():
    # List with both positive and negative numbers
    arr = [3, -2, 5, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.04μs

def test_sorter_floats():
    # List with floating point numbers
    arr = [2.1, 1.5, 3.3, 2.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.21μs -> 3.62μs

def test_sorter_mixed_int_float():
    # List with both ints and floats
    arr = [1, 2.2, 0, 1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 3.50μs

def test_sorter_strings():
    # List of strings (alphabetical order)
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 3.04μs

def test_sorter_strings_case_sensitive():
    # List of strings with different cases
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.04μs

def test_sorter_empty_list():
    # Empty list should remain empty
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.54μs -> 2.79μs

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

def test_sorter_large_numbers():
    # List with very large and very small numbers
    arr = [10**10, -10**10, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.38μs -> 3.42μs

def test_sorter_list_with_none():
    # List containing None should raise TypeError
    arr = [1, None, 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_list_with_incomparable_types():
    # List containing incomparable types (e.g., int and str)
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_list_with_nan():
    # List containing float('nan') should sort, but nan is always unordered
    arr = [1.0, float('nan'), 2.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.29μs

def test_sorter_list_with_inf():
    # List containing float('inf') and float('-inf')
    arr = [1.0, float('inf'), -2.0, float('-inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.33μs

def test_sorter_stability():
    # Test that the sort is stable for equal elements (should preserve order)
    class Item:
        def __init__(self, value, tag):
            self.value = value
            self.tag = tag
        def __lt__(self, other):
            return self.value < other.value
        def __gt__(self, other):
            return self.value > other.value
        def __eq__(self, other):
            return self.value == other.value
        def __repr__(self):
            return f"Item({self.value}, '{self.tag}')"
    a = Item(1, 'a')
    b = Item(2, 'b')
    c = Item(1, 'c')
    arr = [a, b, c]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_sorter_custom_objects_without_comparison():
    # List of objects without comparison methods should raise TypeError
    class Obj:
        pass
    arr = [Obj(), Obj()]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_mutates_input():
    # The function sorts in-place, so the input list is mutated
    arr = [2, 1]
    sorter(arr)

def test_sorter_return_is_input():
    # The returned object should be the same as the input (in-place sort)
    arr = [2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 3.96μs -> 2.88μs

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

def test_sorter_large_random_list():
    # Sorting a large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.1ms -> 60.2μs

def test_sorter_large_sorted_list():
    # Sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.7ms -> 28.8μs

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 # 30.6ms -> 29.1μs

def test_sorter_large_duplicates():
    # 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 -> 49.5μs

def test_sorter_large_strings():
    # 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 # 30.3ms -> 92.8μs

def test_sorter_large_negative_numbers():
    # Sorting a large list of negative numbers
    arr = [random.randint(-10000, -1) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.5ms -> 63.2μs

def test_sorter_large_floats():
    # Sorting a large list of floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.2ms -> 285μs
# 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-mc02cg7b and push.