⚡️ Speed up function mysorter by 71,640%

[codeflash-ai]

📄 71,640% (716.40x) speedup for mysorter in codeflash/bubble_sort.py

⏱️ Runtime : 2.07 seconds → 2.89 milliseconds (best of 94 runs)

📝 Explanation and details

The optimization replaces a manual bubble sort implementation with Python's built-in arr.sort() method. Here's why this achieves a dramatic 71,639% speedup:

Key Changes:

• Eliminated nested loops that performed O(n²) comparisons and swaps
• Replaced manual element swapping with Python's optimized Timsort algorithm
• Reduced from ~38 million operations to a single sort() call

Why This is Much Faster:

1. Algorithm Complexity: Bubble sort is O(n²) while Timsort is O(n log n) on average, with O(n) performance on already-sorted data
2. Native Implementation: Python's sort() is implemented in C and highly optimized for various data patterns
3. Eliminated Python Overhead: The original code had millions of Python bytecode operations for comparisons, assignments, and loop iterations

Performance by Test Case Type:

• Small lists (≤10 elements): Modest 5-20% improvements due to reduced overhead
• Large random lists (1000 elements): Massive 10,000-40,000% speedups where algorithmic complexity dominates
• Already sorted lists: Exceptional 50,000+% improvements as Timsort detects sorted runs in O(n) time
• Reverse sorted lists: Up to 90,000% faster as Timsort efficiently handles this pattern

The optimization maintains identical behavior - sorting in-place and returning the sorted array - while leveraging decades of sorting algorithm research built into Python's standard library.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 3 Passed
🌀 Generated Regression Tests	✅ 61 Passed
⏪ Replay Tests	✅ 2 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_bubble_sort.py::test_sort	1.49s	272μs	✅547273%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large test cases
import string  # used for string sorting tests
import sys  # used for edge cases with int limits

# 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([]) # 12.0μs -> 11.2μs (7.09% faster)

def test_single_element():
    # Test sorting a list with one element
    codeflash_output = mysorter([42]) # 11.9μs -> 11.3μs (5.17% faster)

def test_two_elements_sorted():
    # Test sorting a list with two elements, already sorted
    codeflash_output = mysorter([1, 2]) # 12.2μs -> 11.1μs (10.1% faster)

def test_two_elements_unsorted():
    # Test sorting a list with two elements, unsorted
    codeflash_output = mysorter([2, 1]) # 12.2μs -> 11.1μs (9.37% faster)

def test_multiple_elements_sorted():
    # Test sorting a list with multiple elements, already sorted
    codeflash_output = mysorter([1, 2, 3, 4, 5]) # 12.6μs -> 11.2μs (12.6% faster)

def test_multiple_elements_reverse():
    # Test sorting a list with multiple elements, reverse order
    codeflash_output = mysorter([5, 4, 3, 2, 1]) # 13.3μs -> 11.2μs (19.4% faster)

def test_multiple_elements_unsorted():
    # Test sorting a list with multiple elements, random order
    codeflash_output = mysorter([3, 1, 4, 5, 2]) # 13.0μs -> 11.0μs (17.7% faster)

def test_with_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = mysorter([3, 1, 2, 3, 2, 1]) # 13.3μs -> 11.3μs (17.7% faster)

def test_with_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = mysorter([-3, -1, -2, 0, 2, 1]) # 13.0μs -> 11.4μs (14.7% faster)

def test_with_all_equal_elements():
    # Test sorting a list where all elements are the same
    codeflash_output = mysorter([7, 7, 7, 7]) # 12.1μs -> 11.1μs (8.61% faster)

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

def test_large_negative_and_positive():
    # Test sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999, -1000]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 14.3μs -> 11.8μs (22.0% faster)

def test_with_floats():
    # Test sorting a list with floating point numbers
    arr = [3.1, 2.2, 5.5, 4.4, 1.0]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 14.3μs -> 12.3μs (16.6% faster)

def test_with_mixed_int_float():
    # Test sorting a list with a mix of ints and floats
    arr = [1, 3.3, 2, 4.4, 0]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 14.3μs -> 12.0μs (19.9% faster)

def test_with_strings():
    # Test sorting a list of single-character strings
    arr = ['d', 'a', 'c', 'b']
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 13.1μs -> 11.3μs (15.9% faster)

def test_with_strings_case_sensitive():
    # Test sorting a list of strings with different cases
    arr = ['apple', 'Banana', 'banana', 'Apple']
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 13.0μs -> 11.4μs (14.2% faster)

def test_with_empty_strings():
    # Test sorting a list with empty strings and non-empty strings
    arr = ['', 'a', '', 'b']
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 12.8μs -> 11.2μs (13.3% faster)

def test_with_booleans():
    # Test sorting a list with booleans (False < True)
    arr = [True, False, True, False]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 12.8μs -> 11.2μs (14.1% faster)

def test_with_none_raises_typeerror():
    # Test sorting a list containing None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        mysorter(arr[:]) # 54.8μs -> 54.1μs (1.39% faster)

def test_with_unorderable_types_raises_typeerror():
    # Test sorting a list with incompatible types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        mysorter(arr[:]) # 54.5μs -> 53.8μs (1.32% faster)

def test_with_mutable_elements():
    # Test sorting a list of lists (which are compared lexicographically)
    arr = [[2, 3], [1, 2], [2], [1]]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 13.9μs -> 11.9μs (16.4% faster)

def test_with_custom_objects_raises_typeerror():
    # Test sorting a list of custom objects without __lt__ should raise TypeError
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        mysorter(arr[:]) # 55.0μs -> 54.1μs (1.54% faster)

def test_stability():
    # Test that sorting is stable (equal elements retain their original order)
    arr = [('a', 2), ('b', 1), ('c', 2), ('d', 1)]
    # Sort by the second element
    arr_to_sort = arr[:]
    expected = sorted(arr, key=lambda x: x[1])
    # Decorate the tuples so their comparison is by the second element
    def key_cmp(x): return x[1]
    # Patch mysorter to sort by key, since original only sorts by value
    # So, we sort by the second element using a custom key
    # But since mysorter does not support key, this test is not applicable unless we sort by the tuple itself
    # So, let's test that tuples are sorted lexicographically
    arr = [(2, 'a'), (1, 'b'), (2, 'c'), (1, 'd')]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 14.1μs -> 12.1μs (16.1% faster)

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

def test_large_random_integers():
    # Test sorting a large list of random integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 46.5ms -> 120μs (38394% faster)

def test_large_sorted_input():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = arr[:]
    codeflash_output = mysorter(arr[:]) # 32.6ms -> 59.8μs (54457% faster)

def test_large_reverse_sorted_input():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 53.9ms -> 59.7μs (90149% faster)

def test_large_identical_elements():
    # Test sorting a large list of identical elements
    arr = [42] * 1000
    expected = [42] * 1000
    codeflash_output = mysorter(arr[:]) # 32.4ms -> 57.2μs (56472% faster)

def test_large_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[:]) # 52.2ms -> 143μs (36258% faster)

def test_large_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 46.7ms -> 409μs (11291% faster)

def test_large_booleans():
    # Test sorting a large list of booleans
    arr = [random.choice([True, False]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 41.9ms -> 70.2μs (59557% faster)

# ---------------------------
# In-place vs Return Value
# ---------------------------

def test_inplace_behavior():
    # Test that the function sorts the list in-place (modifies input)
    arr = [3, 2, 1]
    arr_copy = arr[:]
    codeflash_output = mysorter(arr); result = codeflash_output # 13.0μs -> 11.3μs (14.8% faster)

# ---------------------------
# Miscellaneous
# ---------------------------

def test_list_of_tuples():
    # Test sorting a list of tuples (lexicographical order)
    arr = [(2, 3), (1, 4), (2, 2), (1, 3)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]) # 14.3μs -> 12.1μs (17.9% faster)

def test_list_of_empty_lists():
    # Test sorting a list of empty lists (all equal)
    arr = [[], [], []]
    expected = [[], [], []]
    codeflash_output = mysorter(arr[:]) # 12.5μs -> 11.2μs (11.5% faster)

def test_list_with_nan():
    # Test sorting a list containing float('nan')
    arr = [1, float('nan'), 2]
    # The position of nan is not guaranteed, but the other numbers should be in order
    codeflash_output = mysorter(arr[:]); result = codeflash_output # 12.7μs -> 11.4μs (10.9% faster)
    idx_1 = result.index(1)
    idx_2 = result.index(2)

def test_list_with_infinity():
    # Test sorting a list containing float('inf') and float('-inf')
    arr = [1, float('inf'), 2, float('-inf')]
    expected = sorted(arr)
    codeflash_output = mysorter(arr[:]); result = codeflash_output # 13.8μs -> 11.5μs (19.6% 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 for generating large random lists
import string  # used for string sorting tests
import sys  # used for testing with large/small numbers

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

# unit tests

# 1. Basic Test Cases

def test_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 12.5μs -> 11.2μs (11.5% faster)

def test_single_element():
    # Test sorting a single-element list
    arr = [42]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 12.0μs -> 11.2μs (6.67% faster)

def test_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.0μs -> 11.4μs (13.9% faster)

def test_reverse_sorted_list():
    # Test sorting a reverse-sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.1μs -> 11.3μs (16.2% faster)

def test_duplicates():
    # Test sorting a list with duplicate values
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.6μs -> 11.3μs (20.3% faster)

def test_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-1, -3, 2, 0, -2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.1μs -> 11.4μs (15.0% faster)

def test_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    arr = [5, -10, 0, 3, -2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.2μs -> 11.3μs (16.9% faster)

def test_all_equal_elements():
    # Test sorting a list where all elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 12.2μs -> 11.2μs (9.33% faster)

def test_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 12.8μs -> 11.2μs (13.3% faster)

def test_floats():
    # Test sorting a list of floats
    arr = [3.2, 1.5, 2.8, 1.5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 15.0μs -> 12.3μs (22.0% faster)

# 2. Edge Test Cases

def test_large_numbers():
    # Test sorting with very large numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 14.3μs -> 11.8μs (20.8% faster)

def test_small_numbers():
    # Test sorting with very small numbers (floats)
    arr = [1e-10, -1e-10, 0.0, 1e-20, -1e-20]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 16.2μs -> 13.7μs (18.3% faster)

def test_already_sorted_with_duplicates():
    # Test sorting a list that's already sorted but contains duplicates
    arr = [1, 1, 2, 2, 3, 3]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.1μs -> 11.3μs (15.9% faster)

def test_all_negative():
    # Test sorting a list with all negative numbers
    arr = [-5, -1, -3, -2, -4]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.0μs -> 11.3μs (14.3% faster)

def test_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ["α", "β", "γ", "δ", "ε"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 14.1μs -> 11.8μs (19.4% faster)

def test_case_sensitive_strings():
    # Test sorting a list of strings with different cases
    arr = ["apple", "Banana", "banana", "Apple"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.2μs -> 11.5μs (14.4% faster)

def test_empty_strings():
    # Test sorting a list containing empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 12.8μs -> 11.3μs (13.2% faster)

def test_non_comparable_types():
    # Test sorting a list with non-comparable types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 55.0μs -> 53.8μs (2.25% faster)

def test_mutable_elements():
    # Test sorting a list of mutable elements (lists)
    arr = [[3], [1, 2], [2]]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.2μs -> 11.7μs (12.8% faster)

def test_tuple_elements():
    # Test sorting a list of tuples
    arr = [(2, 3), (1, 2), (2, 2)]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 13.3μs -> 11.8μs (12.7% faster)

def test_none_in_list():
    # Test sorting a list with None and numbers should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 54.9μs -> 53.9μs (1.86% faster)

# 3. Large Scale Test Cases

def test_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 32.8ms -> 59.9μs (54657% 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 # 53.9ms -> 60.0μs (89791% faster)

def test_large_random_list():
    # Test sorting a large list of random integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 46.9ms -> 124μs (37538% 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()); result = codeflash_output # 42.1ms -> 98.7μs (42576% faster)

def test_large_string_list():
    # 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()); result = codeflash_output # 52.1ms -> 142μs (36451% faster)

def test_large_float_list():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 45.5ms -> 419μs (10763% 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_bubble_sort.py::test_sort	1.49s	272μs	✅547273%

To edit these changes git checkout codeflash/optimize-mysorter-me64oyo4 and push.