⚡️ Speed up function sorter by 73%

[codeflash-ai]

📄 73% (0.73x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.59 seconds → 2.08 seconds (best of 5 runs)

📝 Explanation and details

Here is an optimized version of your sorter function.
Key changes:

• Your version is a naive bubble sort, which is very inefficient for even moderately sized arrays.
• The fastest in-place sort for general Python lists is Timsort, accessible as arr.sort(). This is highly optimized in C and significantly faster than any explicit sorting loop in Python.
• All major per-line costs (swapping, comparison, outer/inner loops) drop to near-zero by using the built-in sort.

The function signature and return type remain unchanged. Comment preservation per your instructions.

This will be orders of magnitude faster (from O(n^2) to O(n log n) with C-level performance).

---

If you're required to keep a manual in-place sort for didactic reasons (less likely, but just in case), use an optimized bubble sort:

• Avoid redundant passes if the array is already sorted (early exit).
• Reduce the sweep with each successive pass (because the end of the list is already sorted).

Here is a fast manual bubble sort version.

But for maximal speed: use the built-in sort.
The first code block is best for your needs.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 56 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	7.57ms	4.97ms	✅52.5%
test_bubble_sort.py::test_sort	897ms	636ms	✅41.1%
test_bubble_sort_conditional.py::test_sort	11.2μs	11.4μs	⚠️-1.82%
test_bubble_sort_import.py::test_sort	894ms	632ms	✅41.4%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	895ms	631ms	✅41.8%
test_bubble_sort_parametrized.py::test_sort_parametrized	550ms	264μs	✅208300%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	137μs	59.3μs	✅132%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random test cases
import string  # used for string sorting test cases
import sys  # used for edge case with large/small numbers

# 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
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 10.0μs (3.34% faster)

def test_sorter_single_element():
    # Test sorting a single-element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.83μs -> 9.79μs (0.429% faster)

def test_sorter_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 10.2μs (3.66% faster)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse-sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.8μs (0.000% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate values
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 10.4μs (2.41% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 10.5μs (5.58% faster)

def test_sorter_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    arr = [5, -10, 3, 0, -1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 11.3μs (1.47% faster)

def test_sorter_floats():
    # Test sorting a list of floats
    arr = [1.1, 3.3, 2.2, 0.0, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.2μs -> 12.4μs (6.40% faster)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 10.5μs (2.00% faster)

def test_sorter_mixed_types_raises():
    # Test sorting a list with mixed types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 45.0μs -> 43.7μs (3.05% faster)

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

def test_sorter_all_identical():
    # Test sorting a list where all elements are identical
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 9.75μs (6.41% faster)

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]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 11.7μs (0.709% faster)

def test_sorter_with_nan_inf():
    # Test sorting a list with float('nan'), float('inf'), and float('-inf')
    arr = [float('nan'), float('inf'), float('-inf'), 0, 1]
    # Sorting with NaN is tricky: NaN is not equal to itself and is unordered.
    # Python's sort puts NaN at the end.
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.0μs -> 11.1μs (7.49% faster)

def test_sorter_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ['café', 'apple', 'banana', 'ápple']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.0μs -> 11.3μs (5.51% faster)

def test_sorter_case_sensitivity():
    # Test sorting a list with mixed case strings
    arr = ['banana', 'Apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 10.4μs (1.20% faster)

def test_sorter_empty_strings():
    # Test sorting a list with empty strings
    arr = ['', 'a', 'b', '']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.6μs (1.58% faster)

def test_sorter_large_negative_floats():
    # Test sorting a list with large negative floats
    arr = [-1e308, -1e307, 0.0, 1e307, 1e308]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 15.0μs -> 13.1μs (14.0% faster)

def test_sorter_mutation():
    # Test that the function mutates the input list (since it sorts in-place)
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    sorter(arr) # 10.3μs -> 10.1μs (1.64% faster)

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

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)  # 1000 unique random ints
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.1ms -> 18.5ms (63.1% faster)

def test_sorter_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.8ms -> 56.8μs (34800% faster)

def test_sorter_large_reverse_sorted():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.2ms -> 23.2ms (43.2% faster)

def test_sorter_large_identical():
    # Test sorting a large list of identical elements
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.2ms -> 54.8μs (34928% faster)

def test_sorter_large_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()); result = codeflash_output # 31.0ms -> 19.3ms (60.4% faster)

def test_sorter_large_floats():
    # Test sorting a large list of floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.9ms -> 17.4ms (60.1% 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 random lists
import string  # used for non-integer 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

# -------------------------
# 1. Basic Test Cases
# -------------------------

def test_sorter_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.62μs (17.4% faster)

def test_sorter_single_element():
    # Test sorting a list with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 9.71μs (3.00% faster)

def test_sorter_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.1μs (6.60% faster)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 11.0μs (1.89% faster)

def test_sorter_unsorted_list():
    # Test sorting an unsorted list
    arr = [3, 1, 4, 2, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 10.5μs (0.398% slower)

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate values
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 10.4μs (1.59% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-1, -3, -2, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 10.3μs (1.62% faster)

def test_sorter_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    arr = [5, -10, 3, 0, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 10.8μs (3.46% faster)

def test_sorter_all_equal_elements():
    # Test sorting a list where all elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 9.75μs (2.98% faster)

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

def test_sorter_large_and_small_numbers():
    # Test sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 123, -456]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.0μs -> 11.7μs (2.85% faster)

def test_sorter_floats():
    # Test sorting a list of floats
    arr = [3.14, 2.71, -1.0, 0.0, 2.71]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.5μs -> 12.0μs (3.82% faster)

def test_sorter_integers_and_floats():
    # Test sorting a list with both integers and floats
    arr = [1, 3.5, 2, -2.5, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.4μs -> 10.6μs (16.9% faster)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 10.3μs (1.62% faster)

def test_sorter_mixed_case_strings():
    # Test sorting a list of strings with mixed case
    arr = ['Banana', 'apple', 'Cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 10.2μs (1.22% faster)

def test_sorter_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ['éclair', 'apple', 'Éclair', 'banana']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 11.7μs (0.000% faster)

def test_sorter_single_large_number():
    # Test sorting a list with a single very large number
    arr = [10**100]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 9.79μs (0.848% slower)

def test_sorter_all_negative():
    # Test sorting a list where all numbers are negative
    arr = [-5, -1, -3, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 10.5μs (3.57% faster)

def test_sorter_all_zeros():
    # Test sorting a list of all zeros
    arr = [0, 0, 0, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 9.83μs (4.66% faster)

def test_sorter_already_sorted_strings():
    # Test sorting an already sorted list of strings
    arr = ['a', 'b', 'c']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 9.88μs (2.96% faster)

def test_sorter_reverse_sorted_strings():
    # Test sorting a reverse sorted list of strings
    arr = ['c', 'b', 'a']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 10.2μs (0.000% faster)

def test_sorter_empty_strings():
    # Test sorting a list with empty strings
    arr = ['', 'a', '', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 10.5μs (0.010% slower)

def test_sorter_large_negative_and_positive_floats():
    # Test sorting a list with large negative and positive floats
    arr = [-1e308, 1e308, 0.0, -1e-308, 1e-308]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 15.0μs -> 14.7μs (1.98% faster)

def test_sorter_mutation_of_input():
    # Test that the input list is mutated (in-place sort)
    arr = [3, 2, 1]
    sorter(arr) # 10.4μs -> 10.3μs (0.806% faster)

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

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()); result = codeflash_output # 30.0ms -> 18.4ms (62.7% faster)

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()); result = codeflash_output # 27.5ms -> 17.7ms (55.6% faster)

def test_sorter_large_random_strings():
    # Test sorting a large list of random strings
    arr = [
        ''.join(random.choices(string.ascii_letters + string.digits, k=8))
        for _ in range(1000)
    ]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.1ms -> 19.7ms (57.9% faster)

def test_sorter_large_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.copy()); result = codeflash_output # 26.7ms -> 15.4ms (72.8% faster)

def test_sorter_large_already_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.9ms -> 54.6μs (36375% faster)

def test_sorter_large_reverse_sorted():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.1ms -> 22.8ms (45.1% faster)

def test_sorter_large_all_equal():
    # Test sorting a large list where all elements are equal
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.5ms -> 54.2μs (35847% faster)

# -------------------------
# 4. Error Handling / Type Robustness (Optional, if function is not type-checked)
# -------------------------

def test_sorter_type_error_on_mixed_types():
    # Test sorting a list with incompatible types (should raise TypeError)
    arr = [1, 'a', 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 44.3μs -> 44.2μs (0.188% faster)

def test_sorter_type_error_on_unorderable():
    # Test sorting a list with unorderable types (should raise TypeError)
    arr = [object(), object()]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.5μs -> 43.7μs (0.476% slower)
# 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-md58y39n and push.