⚡️ Speed up function sorter by 109,982%

[codeflash-ai]

📄 109,982% (1,099.82x) speedup for sorter in codeflash/galileo/bubble_sort.py

⏱️ Runtime : 1.97 seconds → 1.79 milliseconds (best of 570 runs)

📝 Explanation and details

Here is a much faster version of the program that preserves the print statements and output format.
The original code was using a naive bubble sort (O(n²)). Python's built-in list.sort() is implemented with Timsort (O(n log n)), which is much faster.

This will return exactly the same sorted list, but vastly more efficiently. All comments are preserved as code was streamlined by replacing the inner loops.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 3 Passed
🌀 Generated Regression Tests	✅ 57 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
test_bubble_sort.py::test_sort	1.43s	252μs	✅567045%

🌀 Generated Regression Tests and Runtime

import random  # used for large-scale random test cases
import sys  # used for edge case with sys.maxsize

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

# unit tests

# -----------------
# BASIC TEST CASES
# -----------------

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 5.29μs -> 4.00μs (32.3% faster)

def test_sorter_single_element():
    # Test sorting a list with a single element
    codeflash_output = sorter([42]) # 5.17μs -> 4.12μs (25.3% faster)

def test_sorter_already_sorted():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 6.08μs -> 4.21μs (44.5% faster)

def test_sorter_reverse_sorted():
    # Test sorting a reverse-sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 6.25μs -> 4.21μs (48.5% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 2, 5]) # 5.88μs -> 4.08μs (43.9% faster)

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = sorter([2, 3, 2, 1, 4, 1]) # 6.58μs -> 4.29μs (53.4% faster)

def test_sorter_with_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-1, -3, 2, 0, -2]) # 5.88μs -> 4.33μs (35.6% faster)

def test_sorter_with_all_equal_elements():
    # Test sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7]) # 5.25μs -> 4.21μs (24.8% faster)

def test_sorter_with_two_elements_sorted():
    # Test sorting a two-element list already sorted
    codeflash_output = sorter([1, 2]) # 4.75μs -> 4.08μs (16.3% faster)

def test_sorter_with_two_elements_unsorted():
    # Test sorting a two-element list unsorted
    codeflash_output = sorter([2, 1]) # 4.92μs -> 4.04μs (21.6% faster)

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

def test_sorter_with_large_and_small_numbers():
    # Test sorting a list with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999999, -999999999]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 7.50μs -> 4.67μs (60.7% faster)

def test_sorter_with_floats_and_integers():
    # Test sorting a list with both floats and integers
    arr = [3.5, 2, 4.1, 2.0, -1, 0]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 8.75μs -> 5.21μs (68.0% faster)

def test_sorter_with_negative_zero_and_zero():
    # Test sorting a list with 0 and -0 (should be considered equal in Python)
    arr = [0, -0, 1, -1]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 5.75μs -> 4.25μs (35.3% faster)

def test_sorter_with_minimum_and_maximum_int():
    # Test sorting a list with minimum and maximum integer values
    arr = [sys.maxsize, -sys.maxsize - 1]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 5.33μs -> 4.17μs (28.0% faster)

def test_sorter_with_non_integer_types_raises():
    # Test sorting a list with non-integer types (should raise TypeError)
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr[:])

def test_sorter_with_nan_and_inf():
    # Test sorting a list with float('nan') and float('inf')
    arr = [float('nan'), 1, float('inf'), -float('inf'), 0]
    # Python's sort puts nan at the end, inf at the end, -inf at the start
    expected = sorted(arr, key=lambda x: (x != x, x))
    # The bubble sort in the provided sorter will not handle nan/inf as Python sort does,
    # but for the test, we'll check if the output is sorted except for nan at the end
    codeflash_output = sorter(arr[:]); result = codeflash_output # 7.29μs -> 4.71μs (54.9% faster)

def test_sorter_with_mutable_input():
    # Test that the input list is mutated (in-place sort)
    arr = [3, 1, 2]
    sorter(arr) # 5.42μs -> 4.12μs (31.3% faster)

def test_sorter_with_large_identical_elements():
    # Test sorting a large list with all identical elements
    arr = [5] * 1000
    codeflash_output = sorter(arr[:]) # 31.5ms -> 49.2μs (63838% faster)

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

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr[:]) # 31.6ms -> 52.0μs (60660% faster)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr[:]) # 50.6ms -> 52.1μs (96922% faster)

def test_sorter_large_random_list():
    # Test sorting a large random list
    random.seed(42)
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 46.0ms -> 114μs (40103% faster)

def test_sorter_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[:]) # 40.5ms -> 88.0μs (45957% faster)

def test_sorter_large_list_with_negatives_and_positives():
    # Test sorting a large list with both negative and positive numbers
    arr = [random.randint(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 45.3ms -> 107μs (42102% faster)

# -----------------
# ADDITIONAL EDGE CASES
# -----------------

@pytest.mark.parametrize("arr,expected", [
    ([1, 1, 2, 2, 3, 3], [1, 1, 2, 2, 3, 3]),  # already grouped duplicates
    ([3, 2, 1, 2, 3], [1, 2, 2, 3, 3]),        # unsorted with duplicates
    ([0], [0]),                                 # single zero
    ([0, 0, 0], [0, 0, 0]),                     # all zeros
    ([1, -1, 0], [-1, 0, 1]),                   # mix of negative, zero, positive
])
def test_sorter_various_small_cases(arr, expected):
    # Test various small edge cases
    codeflash_output = sorter(arr[:]) # 4.58μs -> 4.00μs (14.6% 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 test cases
import string  # used for string sorting tests
import sys  # used for maxsize in edge cases

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

# unit tests

# -----------------------
# BASIC TEST CASES
# -----------------------

def test_sorter_simple_integers():
    # Test with a basic unsorted list of positive integers
    arr = [3, 1, 2]
    expected = [1, 2, 3]
    codeflash_output = sorter(arr.copy())

def test_sorter_already_sorted():
    # Test with an already sorted list
    arr = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy())

def test_sorter_reverse_sorted():
    # Test with a reverse sorted list
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy())

def test_sorter_duplicates():
    # Test with duplicates in the list
    arr = [2, 3, 1, 2, 3]
    expected = [1, 2, 2, 3, 3]
    codeflash_output = sorter(arr.copy())

def test_sorter_negative_numbers():
    # Test with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    expected = [-3, -2, -1, 0, 1, 2]
    codeflash_output = sorter(arr.copy())

def test_sorter_mixed_signs():
    # Test with both positive and negative numbers
    arr = [0, -1, 1, -2, 2]
    expected = [-2, -1, 0, 1, 2]
    codeflash_output = sorter(arr.copy())

def test_sorter_single_element():
    # Test with a single element
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy())

def test_sorter_two_elements():
    # Test with two elements, unsorted
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy())

def test_sorter_two_elements_sorted():
    # Test with two elements, already sorted
    arr = [1, 2]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy())

def test_sorter_empty_list():
    # Test with an empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy())

def test_sorter_floats():
    # Test with floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0]
    expected = [1.0, 2.2, 3.1, 5.5]
    codeflash_output = sorter(arr.copy())

def test_sorter_strings():
    # Test with strings (lexicographical order)
    arr = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(arr.copy())

def test_sorter_mixed_case_strings():
    # Test with strings of mixed case (default Python sort is case-sensitive)
    arr = ["Banana", "apple", "Cherry"]
    expected = ["Banana", "Cherry", "apple"]
    codeflash_output = sorter(arr.copy())

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

def test_sorter_all_equal_elements():
    # Test with all elements equal
    arr = [7, 7, 7, 7]
    expected = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy())

def test_sorter_large_and_small_numbers():
    # Test with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    expected = [-sys.maxsize-1, 0, sys.maxsize]
    codeflash_output = sorter(arr.copy())

def test_sorter_nan_and_inf():
    # Test with float('nan') and float('inf')
    arr = [float('inf'), float('-inf'), 0.0]
    # NaN is always sorted to the end in Python's sort, but bubble sort may behave differently
    expected = [float('-inf'), 0.0, float('inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_sorter_with_nan():
    # Test with NaN in the list
    arr = [3.0, float('nan'), 2.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_sorter_with_none():
    # Test with None in the list should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_with_unorderable_types():
    # Test with unorderable types (e.g., int and str)
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_large_negative_numbers():
    # Test with large negative numbers
    arr = [-999999999, -1000000000, -999999998]
    expected = [-1000000000, -999999999, -999999998]
    codeflash_output = sorter(arr.copy())

def test_sorter_mutability():
    # Test that the function sorts in-place (side effect)
    arr = [3, 2, 1]
    sorter(arr)

def test_sorter_stability():
    # Test for stability: equal elements retain their original order
    class Item:
        def __init__(self, value, tag):
            self.value = value
            self.tag = tag
        def __lt__(self, other):
            return self.value < other.value
        def __eq__(self, other):
            return self.value == other.value and self.tag == other.tag
        def __repr__(self):
            return f"Item({self.value},{self.tag})"
    arr = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c')]
    codeflash_output = sorter(arr.copy()); sorted_arr = codeflash_output

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

def test_sorter_large_random_list():
    # Test with a large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy())

def test_sorter_large_sorted_list():
    # Test with a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy())

def test_sorter_large_reverse_sorted_list():
    # Test with a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy())

def test_sorter_large_list_with_duplicates():
    # Test with 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())

def test_sorter_large_strings():
    # Test with 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())

def test_sorter_large_floats():
    # Test with a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy())

def test_sorter_large_list_all_equal():
    # Test with a large list where all elements are equal
    arr = [7] * 1000
    expected = [7] * 1000
    codeflash_output = 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.

To edit these changes git checkout codeflash/optimize-sorter-mcmyop7v and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #488 by aseembits93 was closed.