⚡️ Speed up function sorter by 186,543%

[codeflash-ai]

📄 186,543% (1,865.43x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.74 seconds → 2.00 milliseconds (best of 141 runs)

📝 Explanation and details

Here's an optimized rewrite of your sorter function. The original implementation uses a basic bubble sort algorithm, which is O(n²) time complexity. Python offers a built-in sort (list.sort()), which uses TimSort and is much faster (O(n log n)). The function signature and print statements are preserved, as required.

Explanation:

• Replaced the nested bubble sort loops with the highly optimized .sort() method (in-place).
• The output and function signature are unchanged.
• No unnecessary temporary variables or extra memory allocations.

This is the most efficient way to sort a list in Python 3.12.10, both in terms of runtime and memory for general use.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 49 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	7.58ms	23.9μs	✅31599%
test_bubble_sort.py::test_sort	952ms	157μs	✅602961%
test_bubble_sort_conditional.py::test_sort	12.0μs	8.58μs	✅39.3%
test_bubble_sort_import.py::test_sort	939ms	160μs	✅587009%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	936ms	157μs	✅593668%
test_bubble_sort_parametrized.py::test_sort_parametrized	574ms	157μs	✅363511%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	141μs	51.9μs	✅172%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string test cases
import sys  # used for edge numeric values

# 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_basic_integers_sorted():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 11.2μs -> 8.54μs (31.2% faster)

def test_sorter_basic_integers_unsorted():
    # Unsorted list should be sorted in ascending order
    arr = [5, 3, 1, 4, 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.4μs -> 8.88μs (16.9% faster)

def test_sorter_basic_negative_integers():
    # List with negative integers
    arr = [-3, -1, -2, 0, 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 9.67μs -> 8.54μs (13.2% faster)

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 9.58μs -> 8.88μs (7.99% faster)

def test_sorter_basic_floats():
    # List with float numbers
    arr = [3.2, 1.5, 2.8, 0.1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 13.3μs -> 9.38μs (41.8% faster)

def test_sorter_basic_strings():
    # List of strings (alphabetical order)
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 11.3μs -> 8.62μs (30.9% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.2μs -> 9.25μs (9.91% faster)

def test_sorter_basic_empty():
    # Empty list should return empty
    arr = []
    codeflash_output = sorter(arr[:]); result = codeflash_output # 9.62μs -> 9.12μs (5.48% faster)

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

def test_sorter_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 9.50μs -> 9.33μs (1.78% faster)

def test_sorter_large_negative_and_positive():
    # Large negative and positive integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999, -999999]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 11.2μs -> 8.83μs (27.4% faster)

def test_sorter_already_sorted_reverse():
    # List sorted in reverse order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.0μs -> 8.42μs (18.8% faster)

def test_sorter_strings_case_sensitive():
    # Strings with different cases (should sort lexicographically)
    arr = ["banana", "Apple", "cherry", "apple"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 10.0μs -> 8.54μs (17.1% faster)

def test_sorter_mixed_types_raises():
    # List with mixed types should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr[:])

def test_sorter_nan_and_inf():
    # List with float('nan'), float('inf'), float('-inf')
    arr = [float('nan'), float('inf'), float('-inf'), 0]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 11.5μs -> 9.25μs (24.3% faster)

def test_sorter_unicode_strings():
    # Unicode strings should be sorted correctly
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 11.8μs -> 9.00μs (31.0% faster)

def test_sorter_very_large_numbers():
    # Very large numbers (beyond 64-bit int)
    arr = [10**100, 10**50, -10**100, 0]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 12.5μs -> 10.5μs (19.0% faster)

def test_sorter_mutation():
    # Ensure the input list is mutated (in-place), not just returning new list
    arr = [3, 1, 2]
    sorter(arr)

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

def test_sorter_large_random_integers():
    # Large list of random integers (size 1000)
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 30.5ms -> 67.4μs (45144% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr[:]); result = codeflash_output # 21.0ms -> 36.1μs (57970% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr[:]); result = codeflash_output # 34.7ms -> 37.1μs (93431% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 27.8ms -> 56.5μs (49172% faster)

def test_sorter_large_strings():
    # 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[:]); result = codeflash_output # 33.8ms -> 96.4μs (34934% faster)

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 30.2ms -> 294μs (10164% 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 test cases
import sys  # used for maxsize edge case

# 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_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 9.58μs (27.4% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 8.96μs (31.2% faster)

def test_sorter_basic_unsorted():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 9.38μs (20.0% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 8.38μs (30.4% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 9.25μs (9.01% faster)

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [9, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.33μs (21.0% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 9.25μs (18.5% faster)

def test_sorter_basic_mixed_types():
    # List with floats and ints
    arr = [3.2, 1, 2.5, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.8μs -> 10.1μs (36.6% faster)

# 2. Edge Test Cases

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.75μs -> 8.88μs (9.86% faster)

def test_sorter_edge_all_equal():
    # All elements equal
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 9.38μs (11.6% faster)

def test_sorter_edge_large_numbers():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.7μs -> 9.88μs (28.7% faster)

def test_sorter_edge_minimal_input():
    # Minimal input: list of length 0 and 1
    codeflash_output = sorter([]) # 8.12μs -> 7.67μs (5.97% faster)
    codeflash_output = sorter([1]) # 8.12μs -> 7.67μs (5.97% faster)

def test_sorter_edge_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 9.54μs (14.9% faster)

def test_sorter_edge_unicode_strings():
    # List with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 9.92μs (21.9% faster)

def test_sorter_edge_mixed_signs():
    # List with both positive and negative numbers, including zero
    arr = [0, -1, 1, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 8.75μs (30.5% faster)

def test_sorter_edge_already_sorted():
    # List already sorted
    arr = list(range(10))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 8.46μs (44.8% faster)

def test_sorter_edge_reverse_sorted():
    # List sorted in descending order
    arr = list(range(10, 0, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.9μs -> 8.83μs (57.1% faster)

def test_sorter_edge_stability():
    # Test for stability with tuples (sort by first element)
    arr = [(2, 'b'), (1, 'a'), (2, 'a'), (1, 'b')]
    # This will raise TypeError in Python 3 because tuples are compared elementwise,
    # so this will sort fine since first elements are numbers.
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.3μs -> 10.3μs (19.8% faster)

def test_sorter_edge_type_error():
    # List with incomparable types should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_mutation():
    # Ensure input list is mutated (since the implementation does in-place sorting)
    arr = [3, 2, 1]
    sorter(arr)

# 3. Large Scale Test Cases

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.0ms -> 35.3μs (56473% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.4ms -> 36.6μs (93841% faster)

def test_sorter_large_random():
    # Large random list
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.4ms -> 66.3μs (47192% faster)

def test_sorter_large_duplicates():
    # 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 # 27.8ms -> 56.3μs (49187% faster)

def test_sorter_large_strings():
    # 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 # 32.9ms -> 96.1μs (34096% 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-mc5ezv46 and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #358 by aseembits93 was closed.