⚡️ Speed up function mysorter by 35,154%

[codeflash-ai]

📄 35,154% (351.54x) speedup for mysorter in codeflash/bubble_sort.py

⏱️ Runtime : 360 milliseconds → 1.02 milliseconds (best of 598 runs)

📝 Explanation and details

The optimization replaces the manual O(n²) bubble sort implementation with Python's built-in arr.sort() method, which uses Timsort - a highly optimized hybrid stable sorting algorithm with O(n log n) complexity.

Key changes:

• Eliminated nested loops that performed ~14 million iterations for large inputs
• Replaced manual element swapping with optimized C-level sorting
• Reduced algorithm complexity from O(n²) to O(n log n)

Why this leads to dramatic speedup:
The original bubble sort has quadratic time complexity, making ~n²/2 comparisons and up to n²/2 swaps. For a 1000-element array, this means ~500,000 operations. Python's Timsort leverages existing order in data and uses optimized merge strategies, requiring only ~10,000 operations for the same input.

Performance characteristics by test case:

• Small lists (≤10 elements): 20-75% faster due to reduced overhead
• Large sorted/reverse-sorted lists: 60,000-100,000% faster as Timsort detects existing runs
• Large random lists: 45,000-52,000% faster from algorithmic improvement
• Lists with duplicates: Excellent performance as Timsort handles equal elements efficiently

The line profiler shows the original code spent 95% of time in comparison and swapping operations, while the optimized version completes all sorting in a single optimized call.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 54 Passed
⏪ Replay Tests	✅ 1 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string test cases
import sys  # used for maxsize in edge cases

# imports
import pytest  # used for our unit tests
from codeflash.bubble_sort import mysorter

# unit tests

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

def test_empty_list():
    # Test sorting an empty list
    codeflash_output = mysorter([]) # 3.67μs -> 2.96μs (23.9% faster)

def test_single_element():
    # Test sorting a list with a single element
    codeflash_output = mysorter([42]) # 3.92μs -> 3.00μs (30.6% faster)

def test_sorted_list():
    # Test sorting an already sorted list
    codeflash_output = mysorter([1, 2, 3, 4, 5]) # 4.54μs -> 3.12μs (45.3% faster)

def test_reverse_sorted_list():
    # Test sorting a reverse sorted list
    codeflash_output = mysorter([5, 4, 3, 2, 1]) # 4.96μs -> 3.04μs (63.0% faster)

def test_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = mysorter([3, 1, 4, 5, 2]) # 4.71μs -> 3.04μs (54.8% faster)

def test_list_with_duplicates():
    # Test sorting a list with duplicate values
    codeflash_output = mysorter([4, 2, 5, 2, 3, 4]) # 4.83μs -> 3.08μs (56.8% faster)

def test_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = mysorter([-3, -1, -2, 0, 2, 1]) # 4.38μs -> 3.21μs (36.4% faster)

def test_mixed_positive_and_negative():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = mysorter([0, -2, 5, -1, 3, -4]) # 4.92μs -> 3.21μs (53.3% faster)

def test_all_equal_elements():
    # Test sorting a list where all elements are the same
    codeflash_output = mysorter([7, 7, 7, 7, 7]) # 3.96μs -> 2.92μs (35.7% faster)

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

def test_large_numbers():
    # Test sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999999, -999999999]
    expected = [-sys.maxsize - 1, -999999999, 0, 999999999, sys.maxsize]
    codeflash_output = mysorter(arr) # 5.83μs -> 3.58μs (62.8% faster)

def test_floats():
    # Test sorting a list of floats
    arr = [3.14, 2.71, -1.0, 0.0, 2.71]
    expected = [-1.0, 0.0, 2.71, 2.71, 3.14]
    codeflash_output = mysorter(arr) # 6.25μs -> 3.79μs (64.9% faster)

def test_strings():
    # Test sorting a list of strings lexicographically
    arr = ["banana", "apple", "cherry", "date"]
    expected = ["apple", "banana", "cherry", "date"]
    codeflash_output = mysorter(arr) # 4.83μs -> 3.25μs (48.7% faster)

def test_mixed_types_raises():
    # Test that sorting a list with mixed types raises TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        mysorter(arr) # 2.54μs -> 2.00μs (27.1% faster)

def test_list_with_none_raises():
    # Test that sorting a list with None raises TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        mysorter(arr) # 2.42μs -> 1.83μs (31.8% faster)

def test_list_with_nan():
    # Test sorting a list with float('nan') values (should sort but nan is always unordered)
    arr = [3, float('nan'), 2]
    codeflash_output = mysorter(arr); result = codeflash_output # 4.67μs -> 3.29μs (41.8% faster)

def test_list_with_infinities():
    # Test sorting a list with float('inf') and float('-inf')
    arr = [1, float('inf'), -2, float('-inf')]
    expected = [float('-inf'), -2, 1, float('inf')]
    codeflash_output = mysorter(arr); result = codeflash_output # 5.83μs -> 3.58μs (62.8% faster)

def test_list_with_long_strings():
    # Test sorting a list of long strings
    arr = ["a" * 100, "b" * 50, "a" * 50, "b" * 100]
    expected = ["a" * 50, "a" * 100, "b" * 50, "b" * 100]
    codeflash_output = mysorter(arr) # 6.46μs -> 4.29μs (50.5% faster)

def test_list_with_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ["café", "apple", "banana", "ápple"]
    expected = ["apple", "banana", "café", "ápple"]
    codeflash_output = mysorter(arr) # 5.42μs -> 3.71μs (46.1% faster)

def test_list_with_empty_strings():
    # Test sorting a list with empty strings and normal strings
    arr = ["", "a", "abc", ""]
    expected = ["", "", "a", "abc"]
    codeflash_output = mysorter(arr) # 4.83μs -> 3.29μs (46.8% faster)

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

def test_large_sorted_list():
    # Test sorting a large already sorted list (performance & correctness)
    arr = list(range(1000))
    codeflash_output = mysorter(arr.copy()) # 18.5ms -> 28.8μs (64128% faster)

def test_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = mysorter(arr.copy()) # 30.7ms -> 28.6μs (107271% faster)

def test_large_random_list():
    # Test sorting a large random list
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 28.7ms -> 55.5μs (51580% 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 = mysorter(arr.copy()) # 25.1ms -> 49.2μs (50863% 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 = mysorter(arr.copy()) # 29.5ms -> 90.0μs (32680% faster)

def test_large_list_all_equal():
    # Test sorting a large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = mysorter(arr.copy()) # 18.1ms -> 27.5μs (65881% faster)

def test_large_list_negative_and_positive():
    # Test sorting a large list with both negative and positive integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()) # 27.5ms -> 58.5μs (46956% faster)

# -------------------------
# Mutability/Reference Test
# -------------------------

def test_inplace_sorting_behavior():
    # Test that the function sorts the list in place (if that's the intended behavior)
    arr = [3, 2, 1]
    codeflash_output = mysorter(arr); result = codeflash_output # 4.67μs -> 2.92μs (60.0% faster)

# -------------------------
# Stability Test
# -------------------------

def test_sort_stability():
    # Test that the sort is stable for objects with equal keys
    class Item:
        def __init__(self, key, value):
            self.key = key
            self.value = value
        def __lt__(self, other):
            return self.key < other.key
        def __gt__(self, other):
            return self.key > other.key
        def __eq__(self, other):
            return self.key == other.key and self.value == other.value
        def __repr__(self):
            return f"Item({self.key}, {self.value})"

    items = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c'), Item(2, 'd')]
    # After sorting, items with key=1 should retain their original order ('a', 'c')
    # and items with key=2 should retain their original order ('b', 'd')
    codeflash_output = mysorter(items); sorted_items = codeflash_output # 6.67μs -> 4.12μs (61.6% 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 in large scale tests
import sys  # used for maxsize edge case

# imports
import pytest  # used for our unit tests
from codeflash.bubble_sort import mysorter

# =========================
# Basic Test Cases
# =========================

def test_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 3.62μs -> 2.79μs (29.9% faster)

def test_single_element():
    # Test sorting a list with one element
    arr = [42]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 3.79μs -> 2.79μs (35.9% faster)

def test_already_sorted():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.38μs -> 3.00μs (45.8% faster)

def test_reverse_sorted():
    # Test sorting a reverse-sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.75μs -> 2.92μs (62.9% faster)

def test_duplicates():
    # Test sorting a list with duplicate elements
    arr = [3, 1, 2, 3, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.62μs -> 2.96μs (56.4% faster)

def test_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-1, -3, 2, 0, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.00μs (56.9% faster)

def test_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers and zero
    arr = [0, -2, 5, -1, 3]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.21μs -> 3.00μs (40.3% faster)

def test_all_equal_elements():
    # Test sorting a list where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.29μs -> 2.88μs (49.3% faster)

def test_two_elements_sorted():
    # Test sorting a two-element list already sorted
    arr = [1, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.75μs (40.9% faster)

def test_two_elements_unsorted():
    # Test sorting a two-element list unsorted
    arr = [2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 3.42μs -> 2.83μs (20.6% faster)

# =========================
# Edge Test Cases
# =========================

def test_large_numbers():
    # Test sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize, 0]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.17μs (52.7% faster)

def test_min_max_ints():
    # Test sorting a list with INT_MIN and INT_MAX values
    arr = [-(2**31), 2**31-1, 0]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.42μs -> 3.12μs (41.3% faster)

def test_list_with_zeros():
    # Test sorting a list with multiple zeros
    arr = [0, 0, 0, 0]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.25μs -> 2.92μs (45.7% faster)

def test_alternating_high_low():
    # Test sorting a list with alternating high and low values
    arr = [10, -10, 10, -10, 0]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.12μs (46.7% faster)

def test_floats_and_integers():
    # Test sorting a list with both floats and integers
    arr = [1.5, 2, 0.5, -1, 3.0]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 6.83μs -> 3.92μs (74.4% faster)

def test_mutation_of_input():
    # Test that the function sorts the list in-place (since that's what the implementation does)
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    codeflash_output = mysorter(arr); result = codeflash_output # 4.04μs -> 2.83μs (42.6% faster)

def test_sorting_strings_raises():
    # Test that sorting a list of strings raises a TypeError (since '>' is valid for strings, but let's check)
    arr = ["banana", "apple", "cherry"]
    # This should work for strings, so check correct behavior
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.12μs (46.7% faster)

def test_sorting_mixed_types_raises():
    # Test that sorting a list with mixed types raises a TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 2.46μs -> 1.79μs (37.3% faster)

def test_sorting_none_raises():
    # Test that sorting a list containing None with numbers raises TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 2.25μs -> 1.79μs (25.6% faster)


def test_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 18.4ms -> 29.0μs (63481% faster)

def test_large_reverse_sorted_list():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 30.8ms -> 28.9μs (106596% faster)

def test_large_random_list():
    # Test sorting a large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 27.9ms -> 61.0μs (45600% faster)

def test_large_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 = mysorter(arr.copy()); result = codeflash_output # 25.0ms -> 49.4μs (50625% faster)

def test_large_negative_numbers():
    # Test sorting a large list with negative numbers
    arr = [random.randint(-10000, 0) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 26.8ms -> 60.2μs (44453% faster)

def test_large_floats():
    # Test sorting a large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 27.0ms -> 281μs (9513% faster)

def test_large_alternating():
    # Test sorting a large list with alternating pattern
    arr = [i if i % 2 == 0 else -i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 25.3ms -> 47.0μs (53709% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

⏪ Replay Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_UserscodeflashDownloadscodeflashdevcodeflashcodeflashbubble_sort_py__replay_test_0.py::test_codeflash_bubble_sort_mysorter	5.96μs	3.50μs	✅70.2%

To edit these changes git checkout codeflash/optimize-mysorter-me37bfnb and push.