⚡️ Speed up function sorter by 74%

[codeflash-ai]

📄 74% (0.74x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.68 seconds → 2.11 seconds (best of 5 runs)

📝 Explanation and details

Here is an optimized version. Your original code is an unoptimized bubble sort with unnecessary repeated traversals. We can improve it by.

• Avoiding repeated calls to len(arr) in the loop
• Stopping early if no swaps occur in a pass
• Using tuple unpacking for swapping, which is faster in Python
• Reducing the inner loop traversal on each pass (classic bubble sort optimization)

The fastest general-purpose list sort in Python is the built-in list.sort(), but since function signatures and return values must remain identical, and the question is about code speed-up, here is an improved bubble sort still with explanatory prints.

This is the fastest possible bubble sort using your structure and prints, with minimal memory usage.
If pure speed is needed (and in production, if prints are not strictly required), simply using arr.sort() is vastly faster, but the loop-based sorting is preserved as requested.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 62 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details

- benchmarks/test_benchmark_bubble_sort.py
    - $\color{green}test_sort2: 7.49ms \rightarrow 5.09ms$

- test_bubble_sort.py
    - $\color{green}test_sort: 889ms \rightarrow 638ms$

- test_bubble_sort_conditional.py
    - $\color{green}test_sort: 13.1μs \rightarrow 11.5μs$

- test_bubble_sort_import.py
    - $\color{green}test_sort: 896ms \rightarrow 635ms$

- test_bubble_sort_in_class.py
    - $\color{green}TestSorter.test_sort_in_pytest_class: 893ms \rightarrow 632ms$

- test_bubble_sort_parametrized.py
    - $\color{green}test_sort_parametrized: 548ms \rightarrow 265μs$

- test_bubble_sort_parametrized_loop.py
    - $\color{green}test_sort_loop_parametrized: 135μs \rightarrow 60.4μs$

🌀 Generated Regression Tests Details

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for maxsize in 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 should return an empty list
    codeflash_output = sorter([]) # 9.88μs -> 9.29μs

def test_sorter_single_element():
    # Test sorting a list with one element should return the same single-element list
    codeflash_output = sorter([42]) # 10.9μs -> 10.2μs

def test_sorter_sorted_list():
    # Test sorting an already sorted list should return the same list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 11.2μs -> 10.4μs

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse sorted list should return a sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 11.4μs -> 11.4μs

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 2, 5]) # 10.9μs -> 11.0μs

def test_sorter_duplicates():
    # Test sorting a list with duplicate values
    codeflash_output = sorter([2, 3, 2, 1, 3]) # 10.8μs -> 10.9μs

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([0, -1, 5, -10, 3]) # 11.2μs -> 11.6μs

def test_sorter_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([-2, 4, 0, -1, 3]) # 11.0μs -> 10.8μs

def test_sorter_all_equal_elements():
    # Test sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7]) # 10.3μs -> 10.2μs

def test_sorter_strings():
    # Test sorting a list of strings
    codeflash_output = sorter(['banana', 'apple', 'cherry']) # 11.0μs -> 10.8μs

def test_sorter_floats():
    # Test sorting a list of floating point numbers
    codeflash_output = sorter([3.2, 1.1, 2.5, 0.0]) # 13.1μs -> 11.8μs

def test_sorter_mixed_int_float():
    # Test sorting a list with both integers and floats
    codeflash_output = sorter([3, 1.5, 2, 0.5]) # 12.5μs -> 11.8μs

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

def test_sorter_large_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.copy()) # 11.7μs -> 11.8μs

def test_sorter_minimal_difference():
    # Test sorting a list with numbers that differ by a very small amount
    arr = [1.000001, 1.0000001, 1.0000002, 1.000002]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 12.1μs -> 12.1μs

def test_sorter_strings_case_sensitivity():
    # Test sorting a list of strings with different cases
    arr = ['Apple', 'banana', 'Banana', 'apple']
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 11.2μs -> 10.9μs

def test_sorter_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ['café', 'apple', 'banana', 'ápple']
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 12.0μs -> 11.6μs

def test_sorter_single_character_strings():
    # Test sorting a list of single-character strings
    arr = ['z', 'a', 'm', 'b']
    expected = ['a', 'b', 'm', 'z']
    codeflash_output = sorter(arr.copy()) # 11.5μs -> 11.0μs

def test_sorter_empty_strings():
    # Test sorting a list with empty strings
    arr = ['', 'a', '', 'b']
    expected = ['', '', 'a', 'b']
    codeflash_output = sorter(arr.copy()) # 11.0μs -> 10.8μs

def test_sorter_all_empty_strings():
    # Test sorting a list where all elements are empty strings
    arr = ['', '', '']
    expected = ['', '', '']
    codeflash_output = sorter(arr.copy()) # 9.62μs -> 10.0μs

def test_sorter_large_negative_numbers():
    # Test sorting a list with large negative numbers
    arr = [-1000000, -999999, -1000001]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 11.0μs -> 10.7μs

def test_sorter_already_sorted_large():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 20.3ms -> 58.5μs

def test_sorter_reverse_sorted_large():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 33.2ms -> 22.8ms

def test_sorter_list_with_none_raises():
    # Test that sorting a list with None and numbers raises TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_list_with_incomparable_types_raises():
    # Test that sorting a list with incomparable types raises TypeError
    arr = [1, 'a', 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

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

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = random.sample(range(-100000, -99000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 29.9ms -> 18.2ms

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()) # 28.2ms -> 17.2ms

def test_sorter_large_random_strings():
    # Test sorting a large list of random 5-letter strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 31.3ms -> 19.3ms

def test_sorter_large_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()) # 26.5ms -> 15.7ms

def test_sorter_large_all_same():
    # Test sorting a large list where all elements are the same
    arr = [7] * 1000
    expected = [7] * 1000
    codeflash_output = sorter(arr.copy()) # 19.5ms -> 53.2μs

def test_sorter_large_alternating():
    # Test sorting a large list with alternating high and low values
    arr = [i % 2 for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 23.5ms -> 10.7ms

def test_sorter_large_nearly_sorted():
    # Test sorting a large nearly sorted list with a few elements out of place
    arr = list(range(1000))
    arr[100], arr[101] = arr[101], arr[100]  # swap two elements
    arr[500], arr[501] = arr[501], arr[500]
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 20.2ms -> 78.7μs

def test_sorter_large_reverse_chunks():
    # Test sorting a list made of several large reverse-sorted chunks
    arr = []
    for i in range(0, 1000, 100):
        arr.extend(list(range(i+99, i-1, -1)))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 21.4ms -> 3.35ms
# 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-based tests
import sys  # used for testing with sys.maxsize

# 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 should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.4μs -> 10.2μs

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 11.1μs

def test_sorter_basic_unsorted():
    # Unsorted list with random numbers
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 10.8μs

def test_sorter_basic_duplicates():
    # List with duplicate values
    arr = [4, 2, 5, 2, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 10.7μs

def test_sorter_basic_negatives():
    # List with negative numbers
    arr = [-1, -3, -2, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 10.4μs

def test_sorter_basic_mixed_signs():
    # List with both negative and positive numbers
    arr = [0, -10, 5, 3, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 10.7μs

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 # 10.5μs -> 10.0μs

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 10.1μs


# --- EDGE TEST CASES ---

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.88μs -> 9.29μs

def test_sorter_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.92μs -> 9.46μs

def test_sorter_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 9.83μs

def test_sorter_large_numbers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 11.8μs

def test_sorter_floats():
    # List with floating point numbers
    arr = [3.14, 2.71, -1.0, 0.0, 2.71]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.1μs -> 11.8μs

def test_sorter_mixed_int_float():
    # List with both integers and floats
    arr = [1, 2.5, 0, -3.2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.5μs -> 11.8μs

def test_sorter_strings():
    # List of strings should be sorted lexicographically
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 10.8μs

def test_sorter_single_char_strings():
    # List of single-character strings
    arr = ['d', 'a', 'c', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 10.9μs

def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.0μs -> 11.6μs

def test_sorter_custom_objects_raises():
    # List with non-comparable objects should raise TypeError
    class A:
        pass
    arr = [A(), A()]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_none_in_list_raises():
    # List with None and integers should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_mutation():
    # Ensure the function sorts in-place and returns the same list object
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 10.5μs -> 10.5μs

def test_sorter_list_with_booleans():
    # List with booleans (since bool is a subclass of int)
    arr = [True, False, 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 10.7μs

def test_sorter_list_with_inf_nan():
    # List with float('inf') and float('nan')
    arr = [1, float('inf'), -1, float('nan'), 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 11.5μs

# --- LARGE SCALE TEST CASES ---

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.9ms -> 18.2ms

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.9ms -> 56.1μs

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.4ms -> 22.8ms

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 # 26.5ms -> 15.7ms

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 # 31.4ms -> 19.3ms

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.9ms -> 17.4ms

def test_sorter_large_already_sorted_with_duplicates():
    # Large list, already sorted, with duplicates
    arr = sorted([random.choice(range(100)) for _ in range(1000)])
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.7ms -> 56.9μs

def test_sorter_stability():
    # Test stability: equal elements retain relative order (for objects with key)
    class Item:
        def __init__(self, key, value):
            self.key = key
            self.value = value
        def __lt__(self, other):
            return self.key < other.key
        def __eq__(self, other):
            return self.key == other.key and self.value == other.value
        def __repr__(self):
            return f"Item({self.key}, {self.value})"
    arr = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c'), Item(2, 'd')]
    # Bubble sort is stable, so relative order of equal keys should be preserved
    codeflash_output = sorter(arr.copy()); result = codeflash_output
# 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-mc108vyi and push.