⚡️ Speed up function sorter by 130,321%

[codeflash-ai]

📄 130,321% (1,303.21x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.65 seconds → 2.80 milliseconds (best of 101 runs)

📝 Explanation and details

The optimization replaces a manual bubble sort implementation with Python's built-in arr.sort() method, resulting in a dramatic performance improvement of over 130,000% speedup.

Key Changes:

• Algorithm replacement: Eliminated the O(n²) bubble sort with nested loops in favor of Python's built-in Timsort algorithm (O(n log n) average case)
• Reduced complexity: Replaced ~116M+ operations (from line profiler) with a single optimized C-level sort call

Why This Leads to Speedup:

1. Algorithmic complexity: Bubble sort has O(n²) time complexity, making 116M+ comparisons and swaps for 1000-element arrays. Timsort has O(n log n) complexity, requiring only ~10,000 operations for the same input.

2. Implementation efficiency: Python's sort() is implemented in C and highly optimized, while the original uses interpreted Python loops with expensive array indexing operations.

3. Adaptive behavior: Timsort performs exceptionally well on partially sorted data, which explains why already-sorted large lists see 50,000%+ improvements in the test results.

Test Case Performance Patterns:

• Small arrays (≤10 elements): 10-40% speedup due to function call overhead being more significant
• Large sorted/reverse-sorted arrays: 50,000-90,000% speedup as Timsort's adaptive nature shines
• Large random arrays: 30,000-45,000% speedup from the fundamental algorithmic improvement
• Edge cases: Consistent 5-40% improvements even for error conditions due to faster failure paths

The optimization is universally beneficial, with larger datasets and more structured data (sorted/reverse-sorted) seeing the most dramatic improvements due to Timsort's intelligent handling of existing order.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 61 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	7.52ms	21.7μs	✅34521%
test_bubble_sort.py::test_sort	883ms	158μs	✅557838%
test_bubble_sort_conditional.py::test_sort	11.2μs	7.96μs	✅40.3%
test_bubble_sort_import.py::test_sort	887ms	157μs	✅564775%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	883ms	159μs	✅553148%
test_bubble_sort_parametrized.py::test_sort_parametrized	539ms	158μs	✅340998%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	136μs	50.4μs	✅172%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for min/max integer 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_empty_list():
    # Sorting an empty list should return an empty list
    codeflash_output = sorter([]) # 9.83μs -> 7.83μs (25.5% faster)

def test_single_element():
    # Sorting a single-element list should return the same list
    codeflash_output = sorter([1]) # 9.62μs -> 8.08μs (19.1% faster)

def test_sorted_list():
    # Sorting an already sorted list should return the same list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 10.5μs -> 7.88μs (33.9% faster)

def test_reverse_sorted_list():
    # Sorting a reverse-sorted list should return the sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 10.8μs -> 8.04μs (34.2% faster)

def test_unsorted_list():
    # Sorting a randomly unsorted list should return the sorted list
    codeflash_output = sorter([3, 1, 4, 5, 2]) # 10.5μs -> 8.12μs (29.7% faster)

def test_duplicates():
    # Sorting a list with duplicate elements
    codeflash_output = sorter([3, 1, 2, 3, 2, 1]) # 11.0μs -> 8.29μs (32.7% faster)

def test_negative_numbers():
    # Sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -2, 0, 2, 1]) # 10.7μs -> 8.71μs (23.0% faster)

def test_floats():
    # Sorting a list with floats
    codeflash_output = sorter([3.2, 1.5, 2.7, 2.1]) # 11.3μs -> 9.75μs (16.2% faster)

def test_mixed_int_float():
    # Sorting a list with both ints and floats
    codeflash_output = sorter([3, 1.2, 2, 1.1]) # 11.4μs -> 8.42μs (35.6% faster)

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

def test_all_identical_elements():
    # Sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7]) # 9.96μs -> 8.08μs (23.2% faster)

def test_two_elements_sorted():
    # Sorting a two-element list that is already sorted
    codeflash_output = sorter([1, 2]) # 9.54μs -> 8.17μs (16.8% faster)

def test_two_elements_unsorted():
    # Sorting a two-element list that is not sorted
    codeflash_output = sorter([2, 1]) # 9.38μs -> 8.50μs (10.3% faster)

def test_large_negative_and_positive():
    # Sorting a list with large negative and positive numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999, -999999]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 12.4μs -> 8.92μs (39.3% faster)

def test_strings():
    # Sorting a list of strings lexicographically
    arr = ["banana", "apple", "cherry", "date"]
    expected = ["apple", "banana", "cherry", "date"]
    codeflash_output = sorter(arr.copy()) # 11.2μs -> 8.17μs (37.3% faster)

def test_strings_with_case():
    # Sorting a list of strings with different cases (uppercase before lowercase)
    arr = ["Banana", "apple", "Cherry", "date"]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 10.8μs -> 8.58μs (25.2% faster)

def test_empty_strings():
    # Sorting a list with empty strings
    arr = ["", "a", "b", ""]
    expected = ["", "", "a", "b"]
    codeflash_output = sorter(arr.copy()) # 10.7μs -> 8.62μs (24.2% faster)

def test_list_with_none():
    # Sorting a list with None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 44.1μs -> 42.4μs (4.03% faster)

def test_heterogeneous_types():
    # Sorting a list with incompatible types should raise TypeError
    arr = [1, "string", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.4μs -> 40.1μs (8.32% faster)

def test_large_integers():
    # Sorting a list with very large integers
    arr = [10**18, 10**5, -10**18, 0]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 11.2μs -> 8.38μs (33.8% faster)

def test_list_of_lists():
    # Sorting a list of lists (should sort lexicographically)
    arr = [[2, 3], [1, 2], [1, 1], [2, 2]]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 11.1μs -> 8.79μs (26.5% faster)

def test_list_of_tuples():
    # Sorting a list of tuples (should sort lexicographically)
    arr = [(2, 3), (1, 2), (1, 1), (2, 2)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 11.2μs -> 8.38μs (33.8% faster)

def test_list_of_booleans():
    # Sorting a list of booleans (False < True)
    arr = [True, False, True, False]
    expected = [False, False, True, True]
    codeflash_output = sorter(arr.copy()) # 10.7μs -> 8.79μs (21.3% faster)

def test_unicode_strings():
    # Sorting a list of unicode strings
    arr = ["éclair", "apple", "zebra", "Éclair"]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 11.6μs -> 8.42μs (37.6% faster)

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

def test_large_sorted_list():
    # Sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 19.8ms -> 36.2μs (54637% faster)

def test_large_reverse_sorted_list():
    # Sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 32.9ms -> 35.7μs (92220% faster)

def test_large_random_list():
    # Sorting a large random list of integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 29.9ms -> 71.8μs (41526% faster)

def test_large_duplicates():
    # Sorting a large list with many duplicate values
    arr = [random.choice([0, 1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 27.2ms -> 60.8μs (44736% faster)

def test_large_strings():
    # 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.copy()) # 31.6ms -> 101μs (31183% faster)

def test_large_floats():
    # Sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 28.0ms -> 298μs (9282% faster)

def test_large_mixed_int_float():
    # Sorting a large list of mixed ints and floats
    arr = [random.randint(-1000, 1000) if i % 2 == 0 else random.uniform(-1000, 1000) for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 37.7ms -> 255μs (14652% faster)

# -------------------------
# 4. Mutation Testing Guards
# -------------------------

def test_mutation_guard_not_inplace():
    # Ensure the function sorts in place and returns the same object
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 10.7μs -> 8.75μs (22.4% faster)

def test_mutation_guard_correct_order():
    # Ensure that a single mutation (e.g., > replaced by <) fails
    arr = [5, 3, 4, 1, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 11.0μs -> 8.62μs (27.1% 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 test cases
import string  # used for string sorting tests
import sys  # used for edge value tests

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

# unit tests

# -------------------
# 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 # 10.0μs -> 7.88μs (27.5% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 7.92μs (33.7% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 7.96μs (27.7% faster)

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [2, 3, 1, 2, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 7.88μs (30.2% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-1, -3, 2, 0, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.58μs (19.4% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.79μs -> 8.08μs (8.76% faster)

def test_sorter_basic_two_elements():
    # Two element list
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.17μs -> 8.08μs (13.4% faster)

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.75μs (15.2% faster)

def test_sorter_basic_floats():
    # List of floats
    arr = [2.5, 1.1, 3.3, 2.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 9.79μs (11.5% faster)

def test_sorter_basic_mixed_int_float():
    # List of ints and floats
    arr = [1, 2.2, 0, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 9.08μs (22.5% faster)

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.88μs -> 7.88μs (12.7% faster)

def test_sorter_edge_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 8.12μs (23.1% faster)

def test_sorter_edge_large_numbers():
    # Very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.29μs (22.6% faster)

def test_sorter_edge_strings_case():
    # Strings with different cases
    arr = ["Alpha", "beta", "Gamma", "delta"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 8.58μs (20.9% faster)

def test_sorter_edge_unicode_strings():
    # Strings with unicode characters
    arr = ["ápple", "apple", "äpple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 9.12μs (16.4% faster)

def test_sorter_edge_mutation():
    # Ensure input list is mutated (bubble sort is in-place)
    arr = [2, 1]
    sorter(arr) # 8.83μs -> 8.54μs (3.41% faster)

def test_sorter_edge_not_mutate_copy():
    # Ensure that sorting a copy does not mutate the original
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    sorter(arr_copy) # 9.83μs -> 8.29μs (18.6% faster)

def test_sorter_edge_type_error():
    # List with incompatible types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.8μs -> 41.5μs (5.62% faster)

def test_sorter_edge_nan():
    # List with float('nan') should sort, but nan is always unordered
    arr = [3, float('nan'), 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.83μs -> 8.88μs (10.8% faster)

def test_sorter_edge_inf():
    # List with float('inf') and float('-inf')
    arr = [1, float('inf'), 0, float('-inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.71μs (26.3% faster)

# -------------------
# 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 # 19.9ms -> 37.7μs (52768% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.1ms -> 37.3μs (88487% faster)

def test_sorter_large_random():
    # Large random list
    arr = list(range(1000))
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.4ms -> 69.0μs (42504% 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 # 26.8ms -> 60.5μs (44147% 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 # 31.9ms -> 98.4μs (32263% faster)

def test_sorter_large_negative_numbers():
    # Large list of negative numbers
    arr = [random.randint(-10000, -1) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.8ms -> 72.2μs (39863% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.4ms -> 290μs (10027% faster)

def test_sorter_large_alternating():
    # Large list alternating between two values
    arr = [1 if i % 2 == 0 else 2 for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 23.3ms -> 50.5μs (46074% faster)

def test_sorter_large_already_all_equal():
    # Large list with all elements equal
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.5ms -> 32.2μs (60434% 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-mdp5gjlm and push.