⚡️ Speed up function sorter by 139,489%

[codeflash-ai]

📄 139,489% (1,394.89x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.65 seconds → 2.61 milliseconds (best of 111 runs)

📝 Explanation and details

The optimized code replaces a manual bubble sort implementation with Python's built-in arr.sort() method, achieving a 139,488% speedup (from 3.65 seconds to 2.61 milliseconds).

Key Optimization:

• Replaced O(n²) bubble sort with Timsort: The original code used nested loops with O(n²) time complexity, performing up to 114 million iterations and 53 million swaps for larger inputs. The optimized version uses Python's highly optimized sort() method, which implements Timsort - a hybrid stable sorting algorithm with O(n log n) average case performance.

Why This Works:

• Algorithm complexity: Bubble sort's quadratic time complexity becomes prohibitively expensive as input size grows, while Timsort scales much better
• Native C implementation: Python's sort() is implemented in C and heavily optimized for real-world data patterns
• Adaptive performance: Timsort performs exceptionally well on already sorted or partially sorted data, explaining the massive speedups on ordered inputs (55,692% faster on sorted lists, 91,722% on reverse-sorted)

Test Case Performance:

• Small lists (≤10 elements): Modest 8-36% improvements due to overhead being comparable
• Large lists (1000 elements): Dramatic improvements of 10,000-90,000% faster, demonstrating the quadratic vs linearithmic complexity difference
• Best for: Large datasets, partially sorted data, and any production sorting needs where performance matters

The optimization maintains identical functionality, preserving in-place sorting behavior and all print statements.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 56 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
benchmarks/test_benchmark_bubble_sort.py::test_sort2	7.65ms	23.3μs	✅32701%
test_bubble_sort.py::test_sort	885ms	156μs	✅565555%
test_bubble_sort_conditional.py::test_sort	12.5μs	8.88μs	✅41.3%
test_bubble_sort_import.py::test_sort	892ms	156μs	✅572095%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	896ms	155μs	✅578381%
test_bubble_sort_parametrized.py::test_sort_parametrized	547ms	155μs	✅351419%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	132μs	50.0μs	✅166%

🌀 Generated Regression Tests and Runtime

import random  # used for large scale random input generation
import string  # used for string sorting tests
import sys  # used for maxsize edge 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_sorter_sorted_integers():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.88μs (20.7% faster)

def test_sorter_reverse_sorted_integers():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.75μs (26.2% faster)

def test_sorter_unsorted_integers():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 8.79μs (18.0% faster)

def test_sorter_with_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 8.83μs (23.1% faster)

def test_sorter_negative_and_positive():
    # List with negative and positive numbers
    arr = [-1, 3, 0, -5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 8.92μs (15.9% faster)

def test_sorter_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.42μs -> 8.71μs (8.12% faster)

def test_sorter_two_elements():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.42μs -> 8.62μs (9.17% faster)

def test_sorter_with_floats():
    # List with floats
    arr = [2.5, 1.1, 3.3, 2.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 9.58μs (27.8% faster)

def test_sorter_with_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 8.83μs (16.5% faster)

def test_sorter_with_mixed_case_strings():
    # List of mixed case strings (lexicographical order)
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.83μs (14.6% faster)

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

def test_sorter_empty_list():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.00μs -> 8.58μs (4.86% faster)

def test_sorter_all_identical_elements():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.62μs -> 8.79μs (1.89% slower)

def test_sorter_large_negative_numbers():
    # Large negative numbers
    arr = [-1000000, -999999, -1000001]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.88μs -> 8.88μs (0.000% faster)

def test_sorter_large_positive_numbers():
    # Large positive numbers
    arr = [999999, 1000000, 1000001]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 8.83μs (9.91% faster)

def test_sorter_max_min_int():
    # List with sys.maxsize and -sys.maxsize-1
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 9.12μs (14.2% faster)

def test_sorter_with_nan_and_inf():
    # List with float('nan'), float('inf'), float('-inf')
    arr = [float('nan'), float('inf'), float('-inf'), 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 9.25μs (15.8% faster)
    # NaN is always unordered, so it will end up at the end in Python's sort
    expected = [float('-inf'), 0.0, float('inf'), float('nan')]
    for i in range(3):
        pass

def test_sorter_with_empty_strings():
    # List with empty strings and non-empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 8.92μs (16.4% faster)

def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["café", "apple", "banana", "ápple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 9.29μs (8.96% faster)

def test_sorter_list_of_lists():
    # List of lists (should sort lexicographically)
    arr = [[2, 3], [1], [2, 2], [1, 2]]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 9.54μs (17.5% faster)

def test_sorter_list_with_none_raises():
    # List with None and int (should raise TypeError)
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 40.6μs -> 39.6μs (2.52% faster)

def test_sorter_list_with_incomparable_types_raises():
    # List with int and string (should raise TypeError)
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 40.1μs -> 39.4μs (1.80% faster)

def test_sorter_mutation_of_input():
    # Ensure that the input list is mutated (since the function sorts in place)
    arr = [3, 2, 1]
    sorter(arr) # 9.25μs -> 8.79μs (5.21% faster)

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

def test_sorter_large_random_integers():
    # Large list of random integers (size 1000)
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.2ms -> 71.5μs (42113% faster)

def test_sorter_large_sorted_input():
    # Large already sorted list (size 1000)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.4ms -> 36.5μs (55692% faster)

def test_sorter_large_reverse_sorted_input():
    # Large reverse sorted list (size 1000)
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.7ms -> 36.7μs (91722% faster)

def test_sorter_large_identical_elements():
    # Large list of identical elements (size 1000)
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.9ms -> 34.5μs (57551% faster)

def test_sorter_large_random_floats():
    # Large list of random floats (size 1000)
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.0ms -> 292μs (10153% faster)

def test_sorter_large_strings():
    # Large list of random strings (size 1000)
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.9ms -> 98.5μs (33320% faster)

def test_sorter_large_strings_with_duplicates():
    # Large list of random strings with duplicates (size 1000)
    base = ['dup'] * 500 + [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(500)]
    arr = base.copy()
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.2ms -> 96.8μs (31102% faster)

def test_sorter_large_list_of_lists():
    # Large list of lists (size 1000), each sublist has 2 elements
    arr = [[random.randint(0, 100), random.randint(0, 100)] for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 39.4ms -> 244μs (16023% faster)
# 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  # for maxsize in edge 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_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 9.58μs -> 8.71μs (10.0% faster)

def test_single_element():
    # Test sorting a single-element list
    codeflash_output = sorter([42]) # 10.2μs -> 8.83μs (16.0% faster)

def test_already_sorted():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 10.5μs -> 8.79μs (19.0% faster)

def test_reverse_sorted():
    # Test sorting a reverse-sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 10.8μs -> 8.79μs (22.8% faster)

def test_all_equal_elements():
    # Test sorting a list where all elements are equal
    codeflash_output = sorter([7, 7, 7, 7, 7]) # 10.0μs -> 8.67μs (15.9% faster)

def test_typical_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 1, 5, 9, 2]) # 11.3μs -> 8.83μs (28.3% faster)

def test_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -4, -2, 0]) # 9.92μs -> 8.92μs (11.2% faster)

def test_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([5, -10, 0, 3, -2]) # 10.9μs -> 8.88μs (22.5% faster)

def test_floats():
    # Test sorting a list with floating point numbers
    codeflash_output = sorter([3.1, 2.4, -1.2, 0.0, 2.4]) # 11.9μs -> 9.54μs (24.9% faster)

def test_strings():
    # Test sorting a list of strings
    codeflash_output = sorter(['banana', 'apple', 'pear', 'apple']) # 10.6μs -> 9.12μs (16.0% faster)

def test_mixed_case_strings():
    # Test sorting a list of strings with mixed case (lexicographical order)
    codeflash_output = sorter(['Banana', 'apple', 'Pear', 'apple']) # 10.2μs -> 8.92μs (15.0% faster)

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

def test_large_and_small_numbers():
    # Test sorting with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    expected = sorted(arr)
    codeflash_output = sorter(arr) # 11.7μs -> 9.29μs (25.6% faster)

def test_list_with_none():
    # Test sorting a list containing None (should raise TypeError)
    with pytest.raises(TypeError):
        sorter([1, None, 3]) # 40.9μs -> 37.9μs (7.80% faster)

def test_list_with_different_types():
    # Test sorting a list containing different types (should raise TypeError)
    with pytest.raises(TypeError):
        sorter([1, "two", 3]) # 39.9μs -> 39.2μs (1.70% faster)

def test_list_with_nan():
    # Test sorting a list containing float('nan')
    arr = [3, float('nan'), 2]
    # Python's sort puts NaN at the end, but comparison with NaN is always False,
    # so bubble sort may not move it. Let's check that NaN is present and the rest is sorted.
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.2μs -> 9.12μs (11.4% faster)
    # The non-NaN elements should be sorted at the start
    non_nan = [x for x in result if x == x]

def test_list_with_inf():
    # Test sorting a list with positive and negative infinity
    arr = [float('inf'), 1, float('-inf'), 0]
    codeflash_output = sorter(arr) # 11.0μs -> 9.17μs (19.6% faster)

def test_list_with_duplicates():
    # Test sorting a list with many duplicates
    arr = [5, 3, 5, 2, 5, 1, 5]
    codeflash_output = sorter(arr) # 10.7μs -> 8.83μs (20.8% faster)

def test_list_with_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ['café', 'apple', 'banana', 'ápple']
    expected = sorted(arr)
    codeflash_output = sorter(arr) # 11.1μs -> 8.25μs (34.4% faster)


def test_list_with_empty_strings():
    # Test sorting a list with empty strings
    arr = ["", "a", "abc", ""]
    codeflash_output = sorter(arr) # 11.0μs -> 8.08μs (36.1% faster)

def test_list_with_bool():
    # Test sorting a list with boolean values (Python treats False < True < int)
    arr = [True, False, 1, 0]
    # In Python, False==0, True==1, so after sorting: [False, 0, True, 1] == [False, 0, True, 1]
    codeflash_output = sorter(arr) # 10.4μs -> 7.92μs (31.6% faster)

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

def test_large_sorted_list():
    # Test sorting a large already sorted list (performance and correctness)
    arr = list(range(1000))
    codeflash_output = sorter(arr[:]) # 20.4ms -> 33.8μs (60074% faster)

def test_large_reverse_sorted_list():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 33.8ms -> 36.8μs (91901% faster)

def test_large_random_list():
    # Test sorting a large random list of integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 30.6ms -> 71.7μs (42546% faster)

def test_large_list_with_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[:]) # 28.4ms -> 60.2μs (46995% faster)

def test_large_list_of_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[:]) # 32.7ms -> 99.4μs (32842% faster)

def test_large_list_stability():
    # Test sorting stability: tuples (value, index) where value is the key
    arr = [(random.randint(0, 10), i) for i in range(1000)]
    expected = sorted(arr, key=lambda x: x[0])
    # Bubble sort is stable, so indices should be preserved for equal values
    codeflash_output = sorter(arr[:]) # 35.7ms -> 201μs (17670% faster)
# 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-me1sobvx and push.