⚡️ Speed up function sorter by 9%

[codeflash-ai]

📄 9% (0.09x) speedup for sorter in src/async_examples/concurrency.py

⏱️ Runtime : 1.33 seconds → 1.22 seconds (best of 78 runs)

📝 Explanation and details

The optimization replaces a manual O(n²) bubble sort implementation with Python's built-in arr.sort() method, which uses the highly optimized Timsort algorithm with O(n log n) complexity.

Key changes:

• Removed the nested loops that performed bubble sort (lines with ~1.2M hits each in the profiler)
• Replaced the entire sorting logic with a single arr.sort() call

Why this is faster:
The line profiler shows the dramatic impact - the nested loops in the original code executed over 1.2 million times each, consuming 86% of total runtime (lines 5-8 in original profiler results). The optimized version eliminates all this overhead with a single native method call that takes only 2.3% of the new total runtime.

Performance characteristics:

• Small lists: Moderate speedup due to reduced Python bytecode execution overhead
• Large lists: Dramatic speedup due to algorithmic improvement (O(n²) → O(n log n))
• Already sorted lists: Timsort's adaptive nature provides excellent performance on pre-sorted data
• Lists with duplicates: Timsort handles duplicate elements efficiently

The 8% speedup shown here likely represents testing on relatively small arrays where the async overhead dominates. For larger datasets, the speedup would be much more significant due to the fundamental algorithmic improvement.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 107 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import asyncio  # used to run async functions

import pytest  # used for our unit tests
from src.async_examples.concurrency import sorter

# unit tests

# 1. BASIC TEST CASES

@pytest.mark.asyncio
async def test_sorter_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_reverse_sorted_list():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_unsorted_list():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_list_with_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 3]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_single_element():
    # List with a single element should be unchanged
    arr = [42]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_empty_list():
    # Empty list should be unchanged
    arr = []
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-1, -3, -2, 0, 2]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_all_equal_elements():
    # All elements equal
    arr = [7, 7, 7, 7]
    result = await sorter(arr.copy())

# 2. EDGE TEST CASES

@pytest.mark.asyncio
async def test_sorter_large_numbers():
    # List with very large and very small integers
    arr = [2**31-1, -2**31, 0, 999999999, -999999999]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_floats_and_ints():
    # List with both floats and ints
    arr = [3.1, 2, 5.5, 4, 1.0]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_mutation_side_effect():
    # Ensure the returned list is the same object as input (since the function sorts in-place)
    arr = [4, 3, 2, 1]
    result = await sorter(arr)

@pytest.mark.asyncio
async def test_sorter_concurrent_different_inputs():
    # Run multiple sorter coroutines concurrently with different inputs
    arr1 = [5, 3, 1]
    arr2 = [2, 4, 6, 8]
    arr3 = [9, 7, 5, 3, 1]
    results = await asyncio.gather(
        sorter(arr1.copy()),
        sorter(arr2.copy()),
        sorter(arr3.copy())
    )

@pytest.mark.asyncio
async def test_sorter_concurrent_same_input():
    # Run multiple sorter coroutines concurrently on the same input (copy to avoid mutation)
    arr = [4, 2, 3, 1]
    tasks = [sorter(arr.copy()) for _ in range(5)]
    results = await asyncio.gather(*tasks)
    for result in results:
        pass

@pytest.mark.asyncio
async def test_sorter_input_not_mutated_when_copied():
    # Ensure that passing a copy keeps the original list unchanged
    arr = [2, 1]
    _ = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_with_boolean_values():
    # Test with boolean values (since True > False in Python)
    arr = [True, False, True, False]
    result = await sorter(arr.copy())

# 3. LARGE SCALE TEST CASES

@pytest.mark.asyncio
async def test_sorter_large_list():
    # Sort a large list of 1000 elements in reverse order
    arr = list(range(1000, 0, -1))
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_many_concurrent_large_lists():
    # Run sorter concurrently on multiple large lists
    lists = [list(range(n, 0, -1)) for n in range(100, 105)]
    tasks = [sorter(lst.copy()) for lst in lists]
    results = await asyncio.gather(*tasks)
    for n, result in zip(range(100, 105), results):
        pass

@pytest.mark.asyncio
async def test_sorter_stress_many_small_lists():
    # Run sorter concurrently on many small lists
    tasks = [sorter([3, 2, 1]) for _ in range(50)]
    results = await asyncio.gather(*tasks)
    for result in results:
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import asyncio  # used to run async functions

import pytest  # used for our unit tests
from src.async_examples.concurrency import sorter

# -------------------------------
# Basic Test Cases
# -------------------------------

@pytest.mark.asyncio
async def test_sorter_basic_sorted():
    # Test already sorted list
    arr = [1, 2, 3, 4, 5]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_basic_unsorted():
    # Test unsorted list
    arr = [5, 4, 3, 2, 1]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_basic_duplicates():
    # Test list with duplicates
    arr = [3, 1, 2, 3, 2]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_basic_single_element():
    # Test single element list
    arr = [42]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_basic_empty():
    # Test empty list
    arr = []
    result = await sorter(arr.copy())

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

@pytest.mark.asyncio
async def test_sorter_edge_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, -5, 0]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_mixed_types():
    # List with floats and ints
    arr = [3.5, 2, 4.1, 2.0, 3]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_large_numbers():
    # List with very large and very small numbers
    arr = [1e10, 1e-10, 0, -1e10]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_inplace_mutation():
    # Ensure input list is mutated in place
    arr = [2, 1]
    arr_copy = arr.copy()
    await sorter(arr)

@pytest.mark.asyncio
async def test_sorter_edge_concurrent_execution():
    # Run multiple sorter coroutines concurrently
    arrs = [
        [5, 3, 1],
        [10, 9, 8],
        [2, 2, 1],
        [],
        [0]
    ]
    expected = [
        [1, 3, 5],
        [8, 9, 10],
        [1, 2, 2],
        [],
        [0]
    ]
    results = await asyncio.gather(*(sorter(arr.copy()) for arr in arrs))

@pytest.mark.asyncio

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

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

@pytest.mark.asyncio
async def test_sorter_large_scale_sorted():
    # Large sorted list
    arr = list(range(100))
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_large_scale_reverse():
    # Large reverse sorted list
    arr = list(reversed(range(100)))
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_large_scale_duplicates():
    # Large list with many duplicates
    arr = [5] * 50 + [3] * 50 + [7] * 50
    expected = [3] * 50 + [5] * 50 + [7] * 50
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_large_scale_concurrent():
    # Test concurrent sorting of multiple large lists
    arrs = [list(reversed(range(100))) for _ in range(10)]
    expected = [list(range(100)) for _ in range(10)]
    results = await asyncio.gather(*(sorter(arr.copy()) for arr in arrs))

@pytest.mark.asyncio
async def test_sorter_large_scale_random():
    # Large random list
    import random
    arr = [random.randint(0, 1000) for _ in range(200)]
    expected = sorted(arr)
    result = await 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.

To edit these changes git checkout codeflash/optimize-sorter-mffxzvtl and push.