⚡️ Speed up function sorter by 140,685%

[codeflash-ai]

📄 140,685% (1,406.85x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.46 seconds → 2.46 milliseconds (best of 461 runs)

📝 Explanation and details

Here's a much faster version of your program. Your original function is a naive bubble sort (O(n²)); we can improve this significantly.
We'll use Python's built-in list.sort(), which is highly optimized (TimSort, O(n log n)), and operates in-place as your original code does.
We also keep the print statements exactly as before and preserve all comments.

Why this is faster.

• arr.sort() is orders of magnitude faster than a manual bubble sort.
• Memory usage is optimal since sort is in-place, as before.
• Print statements are unchanged, maintaining behavior and output.

This rewrite is the optimal approach in both runtime and memory for your requirements.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 66 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details and Performance Breakdown

• benchmarks/test_benchmark_bubble_sort.py

  • test_sort2: 6.91ms -> 17.2μsdiff +399.84544927536234%

• test_bubble_sort.py

  • test_sort: 826ms -> 141μs$\color{green}(+5848.699346220247%)$

• test_bubble_sort_conditional.py

  • test_sort: 5.29μs -> 3.00μs$\color{green}(+0.764%)$

• test_bubble_sort_import.py

  • test_sort: 822ms -> 140μs$\color{green}(+5854.920814431301%)$

• test_bubble_sort_in_class.py

  • TestSorter.test_sort_in_pytest_class: 823ms -> 144μs$\color{green}(+5711.698229652796%)$

• test_bubble_sort_parametrized.py

  • test_sort_parametrized: 500ms -> 143μs$\color{green}(+3487.9654193548386%)$

• test_bubble_sort_parametrized_loop.py

  • test_sort_loop_parametrized: 100μs -> 20.5μs$\color{green}(+3.895956294814887%)$

🌀 Generated Regression Tests Details and Performance Breakdown

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for testing with min/max int

# 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 should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.12μs

def test_sorter_basic_reverse():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 2.96μs

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 2.96μs

def test_sorter_basic_duplicates():
    # List with duplicate values
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.08μs

def test_sorter_basic_all_equal():
    # List where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 2.92μs

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 2.92μs

def test_sorter_basic_floats():
    # List with floats and integers
    arr = [2.5, 3, 1.1, 2, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.25μs -> 3.62μs

def test_sorter_basic_strings():
    # List of strings
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.04μs

def test_sorter_basic_mixed_case_strings():
    # List of strings with mixed case (lexicographical order)
    arr = ['Banana', 'apple', 'Cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.54μs -> 2.92μs

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

def test_sorter_edge_empty():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.58μs -> 2.88μs

def test_sorter_edge_single_element():
    # Single element list should be unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 3.04μs

def test_sorter_edge_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.75μs

def test_sorter_edge_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.83μs

def test_sorter_edge_min_max_int():
    # List with min and max integer values
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.17μs

def test_sorter_edge_large_negatives_and_positives():
    # List with large negative and positive values
    arr = [-999999, 123456, 0, -123456, 999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 3.17μs

def test_sorter_edge_all_zeroes():
    # List with all zeroes
    arr = [0, 0, 0, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 2.96μs

def test_sorter_edge_floats_with_precision():
    # List with floats with close precision
    arr = [1.000001, 1.0, 1.0000001]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.58μs

def test_sorter_edge_strings_with_empty():
    # List of strings with empty string
    arr = ['z', '', 'a']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 3.04μs

def test_sorter_edge_unicode_strings():
    # List of unicode strings
    arr = ['á', 'a', 'ä', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.42μs -> 3.25μs

def test_sorter_edge_mutation():
    # Ensure that the function mutates the input list (since it sorts in-place)
    arr = [3, 2, 1]
    sorter(arr)

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

def test_sorter_large_sorted():
    # Large sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.4ms -> 29.1μs

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.5ms -> 30.3μs

def test_sorter_large_random():
    # Large random list of integers
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.8ms -> 61.1μs

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 # 24.6ms -> 50.0μs

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 # 29.8ms -> 88.7μs

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.copy()); result = codeflash_output # 27.2ms -> 283μs

def test_sorter_large_all_equal():
    # Large list where all elements are the same
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.1ms -> 27.3μs

def test_sorter_large_alternating():
    # Large list with alternating values
    arr = [0, 1] * 500
    expected = [0]*500 + [1]*500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 21.3ms -> 44.5μs

# ------------------------
# 4. Error/Type Handling (Optional: Documented for completeness)
# ------------------------

def test_sorter_type_error_on_mixed_types():
    # List with mixed types should raise TypeError in Python 3
    arr = [1, 'a', 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_type_error_on_unorderable():
    # List with unorderable types (e.g., int and None)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        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.

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

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

# unit tests

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

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 4.21μs -> 2.96μs

def test_sorter_single_element():
    # Test sorting a list with one element
    codeflash_output = sorter([42]) # 4.08μs -> 2.96μs

def test_sorter_already_sorted():
    # Test sorting a list that's already sorted
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 4.67μs -> 2.96μs

def test_sorter_reverse_sorted():
    # Test sorting a list sorted in reverse order
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 5.00μs -> 3.08μs

def test_sorter_unsorted_integers():
    # Test sorting a typical unsorted list of integers
    codeflash_output = sorter([3, 1, 4, 1, 5, 9, 2]) # 5.50μs -> 3.12μs

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = sorter([2, 3, 2, 1, 3, 1]) # 5.25μs -> 3.08μs

def test_sorter_negative_integers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -4, -2, 0]) # 4.88μs -> 3.04μs

def test_sorter_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([3, -2, 1, 0, -1, 2]) # 5.08μs -> 3.00μs

def test_sorter_floats():
    # Test sorting a list of floating point numbers
    codeflash_output = sorter([3.1, 2.4, 5.6, 1.0]) # 5.92μs -> 3.58μs

def test_sorter_mixed_int_float():
    # Test sorting a list with both ints and floats
    codeflash_output = sorter([1, 3.5, 2, 2.5]) # 5.50μs -> 3.58μs

def test_sorter_strings():
    # Test sorting a list of strings (lexicographical order)
    codeflash_output = sorter(['banana', 'apple', 'cherry']) # 4.88μs -> 3.08μs

def test_sorter_mixed_case_strings():
    # Test sorting strings with mixed case (Python's default is uppercase before lowercase)
    codeflash_output = sorter(['Banana', 'apple', 'Cherry']) # 3.88μs -> 3.04μs

def test_sorter_unicode_strings():
    # Test sorting strings with unicode characters
    codeflash_output = sorter(['éclair', 'apple', 'Éclair']) # 5.42μs -> 3.42μs

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

def test_sorter_all_identical_elements():
    # Test sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7]) # 4.42μs -> 3.00μs

def test_sorter_two_elements_sorted():
    # Test sorting a two-element list that's already sorted
    codeflash_output = sorter([1, 2]) # 3.96μs -> 2.88μs

def test_sorter_two_elements_unsorted():
    # Test sorting a two-element list that is unsorted
    codeflash_output = sorter([2, 1]) # 3.92μs -> 2.88μs

def test_sorter_large_negative_and_positive():
    # Test sorting with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr) # 5.00μs -> 3.17μs

def test_sorter_min_max_float():
    # Test sorting with min and max float values
    arr = [float('inf'), float('-inf'), 0.0, 1.0, -1.0]
    codeflash_output = sorter(arr) # 6.00μs -> 3.58μs

def test_sorter_nan_values():
    # Test sorting with NaN values (NaN is always unordered, but Python's sort leaves NaNs at the end)
    arr = [3.0, float('nan'), 2.0]
    codeflash_output = sorter(arr); result = codeflash_output # 4.33μs -> 3.17μs

def test_sorter_empty_string():
    # Test sorting with empty strings
    arr = ["", "a", "b"]
    codeflash_output = sorter(arr) # 4.46μs -> 3.04μs

def test_sorter_single_character_strings():
    # Test sorting single character strings
    arr = ["z", "a", "m"]
    codeflash_output = sorter(arr) # 4.42μs -> 3.08μs

def test_sorter_list_of_lists():
    # Test sorting a list of lists (lexicographical order)
    arr = [[2, 3], [1, 4], [1, 2]]
    codeflash_output = sorter(arr) # 5.33μs -> 3.46μs

def test_sorter_list_of_tuples():
    # Test sorting a list of tuples (lexicographical order)
    arr = [(2, 3), (1, 4), (1, 2)]
    codeflash_output = sorter(arr) # 5.33μs -> 3.54μs

def test_sorter_heterogeneous_types_error():
    # Test sorting a list with incomparable types should raise TypeError
    arr = [1, "a"]
    with pytest.raises(TypeError):
        sorter(arr)

def test_sorter_none_in_list_error():
    # Test sorting a list with None and other types should raise TypeError
    arr = [None, 1]
    with pytest.raises(TypeError):
        sorter(arr)

def test_sorter_mutates_input():
    # Test that the input list is mutated in place (bubble sort is in-place)
    arr = [2, 1]
    sorter(arr)

def test_sorter_large_identical_elements():
    # Test a large list of identical elements
    arr = [5] * 1000
    codeflash_output = sorter(arr) # 17.7ms -> 27.0μs

# ----------------------
# 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.copy()) # 18.3ms -> 29.1μs

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()) # 30.7ms -> 29.4μs

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 27.9ms -> 60.5μs

def test_sorter_large_random_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 26.0ms -> 290μs

def test_sorter_large_random_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 = sorter(arr.copy()) # 29.6ms -> 87.6μs

def test_sorter_large_mixed_case_strings():
    # Test sorting a large list of mixed-case strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 29.7ms -> 87.5μs

def test_sorter_large_list_with_duplicates():
    # Test sorting a large list with many duplicate values
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 24.3ms -> 49.5μs

def test_sorter_large_list_of_lists():
    # Test sorting a large list of lists (lexicographical)
    arr = [[random.randint(0, 100), random.randint(0, 100)] for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 38.1ms -> 237μs

def test_sorter_large_list_of_tuples():
    # Test sorting a large list of tuples (lexicographical)
    arr = [(random.randint(0, 100), random.randint(0, 100)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 35.9ms -> 188μs
# 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-mc12pohm and push.