⚡️ Speed up function sorter by 1,180%

[codeflash-ai]

📄 1,180% (11.80x) speedup for sorter in src/async_examples/concurrency.py

⏱️ Runtime : 2.84 seconds → 222 milliseconds (best of 233 runs)

📝 Explanation and details

The optimization replaces a manual bubble sort implementation with Python's built-in arr.sort() method, delivering a 1179% speedup by leveraging algorithmic and implementation advantages:

Key Changes:

• Algorithmic improvement: Bubble sort's O(n²) complexity is replaced with Timsort's O(n log n) average case
• Implementation efficiency: The nested Python loops (1.3M+ iterations in profiler) are replaced with optimized C code in a single method call
• Maintained behavior: The function still sorts in-place, preserves async functionality, and keeps all print statements

Performance Analysis:
The line profiler shows the dramatic difference - the original code spent 35.6% of time in the outer loop and 37.1% in comparisons across 1.3M+ iterations. The optimized version replaces all this with a single arr.sort() call taking only 7.6% of the much shorter total runtime.

Test Case Performance:

• Small arrays (basic tests): Benefit from Timsort's adaptive optimizations for nearly-sorted data
• Large arrays (100+ elements): Show the most dramatic improvements due to O(n²) vs O(n log n) complexity difference
• Concurrent workloads (throughput tests): Each individual sort completes much faster, improving overall throughput
• Edge cases (duplicates, reverse-sorted): Timsort handles these patterns exceptionally well with specialized optimizations

The optimization maintains identical functionality while replacing an inefficient algorithm with one of the fastest sorting implementations available.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 441 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 BEGIN ----------------------

# 1. Basic Test Cases

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

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

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

@pytest.mark.asyncio
async def test_sorter_basic_duplicates():
    # List with duplicate values should be sorted, duplicates preserved
    arr = [2, 3, 2, 1, 3]
    result = await sorter(arr.copy())

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

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

@pytest.mark.asyncio
async def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [7, 3]
    result = await sorter(arr.copy())

# 2. Edge Test Cases

@pytest.mark.asyncio
async def test_sorter_edge_all_equal():
    # List with all elements equal
    arr = [5, 5, 5, 5, 5]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, -5]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_mixed_signs():
    # List with both positive and negative numbers
    arr = [0, -1, 2, -3, 4]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_floats():
    # List with float values
    arr = [3.2, 1.5, 4.8, 2.2]
    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, -1e10, 0, 999999999, -999999999]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_concurrent_execution():
    # Test concurrent execution with different inputs
    arrs = [
        [5, 3, 1],
        [2, 4, 6],
        [9, 7, 8]
    ]
    results = await asyncio.gather(*(sorter(arr.copy()) for arr in arrs))

@pytest.mark.asyncio
async def test_sorter_edge_empty_and_nonempty_concurrent():
    # Concurrently sort an empty and a non-empty list
    arr1 = []
    arr2 = [2, 1]
    results = await asyncio.gather(sorter(arr1.copy()), sorter(arr2.copy()))

@pytest.mark.asyncio
async def test_sorter_edge_non_integer_types():
    # List with non-integer types (should sort by Python's default comparison)
    arr = ['c', 'a', 'b']
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_mixed_types_raises():
    # List with mixed types should raise TypeError
    arr = [1, 'a', 3]
    with pytest.raises(TypeError):
        await sorter(arr.copy())

# 3. Large Scale Test Cases

@pytest.mark.asyncio
async def test_sorter_large_scale_100_elements():
    # Sort a list of 100 elements in reverse order
    arr = list(range(100, 0, -1))
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_large_scale_100_random_elements():
    # Sort a list of 100 random elements
    import random
    arr = random.sample(range(1, 1000), 100)
    expected = sorted(arr)
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_large_scale_concurrent_50_lists():
    # Concurrently sort 50 lists of 20 random elements each
    import random
    arrs = [random.sample(range(1000), 20) for _ in range(50)]
    expected = [sorted(arr) for arr in arrs]
    results = await asyncio.gather(*(sorter(arr.copy()) for arr in arrs))

# 4. Throughput Test Cases

@pytest.mark.asyncio
async def test_sorter_throughput_small_load():
    # Throughput test: sort 10 small lists concurrently
    arrs = [[i, i-1, i+1] for i in range(10)]
    expected = [sorted(arr) for arr in arrs]
    results = await asyncio.gather(*(sorter(arr.copy()) for arr in arrs))

@pytest.mark.asyncio
async def test_sorter_throughput_medium_load():
    # Throughput test: sort 50 medium-size lists concurrently
    import random
    arrs = [random.sample(range(1000), 30) for _ in range(50)]
    expected = [sorted(arr) for arr in arrs]
    results = await asyncio.gather(*(sorter(arr.copy()) for arr in arrs))

@pytest.mark.asyncio
async def test_sorter_throughput_high_volume():
    # Throughput test: sort 100 lists of 100 elements each concurrently
    import random
    arrs = [random.sample(range(1000), 100) for _ in range(100)]
    expected = [sorted(arr) for arr in arrs]
    results = await asyncio.gather(*(sorter(arr.copy()) for arr in arrs))

@pytest.mark.asyncio
async def test_sorter_throughput_varying_sizes():
    # Throughput test: sort lists of varying sizes concurrently
    import random
    arrs = [random.sample(range(1000), size) for size in range(1, 51)]
    expected = [sorted(arr) for arr in arrs]
    results = await asyncio.gather(*(sorter(arr.copy()) for arr in arrs))
# 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():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    result = await sorter(arr.copy())

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

@pytest.mark.asyncio
async def test_sorter_basic_duplicates():
    # List with duplicates should be sorted correctly
    arr = [3, 1, 2, 3, 2]
    result = await sorter(arr.copy())

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

@pytest.mark.asyncio
async def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_basic_empty_list():
    # Empty list should remain unchanged
    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, 7]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_reverse_sorted():
    # List sorted in reverse order
    arr = [5, 4, 3, 2, 1]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_large_numbers():
    # List with large integer values
    arr = [100000, 99999, 100001]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_floats():
    # List with float values
    arr = [2.5, 3.1, 1.0, 2.5]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_mixed_types():
    # List with mixed int and float types
    arr = [3, 1.5, 2, 0.5]
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_edge_concurrent_execution():
    # Test concurrent execution with asyncio.gather
    arr1 = [3, 2, 1]
    arr2 = [5, 4, 6]
    arr3 = [0, -1, 1]
    results = await asyncio.gather(
        sorter(arr1.copy()),
        sorter(arr2.copy()),
        sorter(arr3.copy())
    )

@pytest.mark.asyncio
async def test_sorter_edge_mutation():
    # Ensure the input list is sorted in-place (mutation)
    arr = [3, 2, 1]
    await sorter(arr)

@pytest.mark.asyncio
async def test_sorter_edge_empty_and_nonempty_concurrent():
    # Test concurrent execution with empty and non-empty lists
    arr1 = []
    arr2 = [2, 1]
    arr3 = [1]
    results = await asyncio.gather(
        sorter(arr1.copy()),
        sorter(arr2.copy()),
        sorter(arr3.copy())
    )

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

@pytest.mark.asyncio
async def test_sorter_large_scale_100_elements():
    # Large list of 100 elements shuffled
    arr = list(range(100))
    import random
    random.shuffle(arr)
    result = await sorter(arr.copy())

@pytest.mark.asyncio
async def test_sorter_large_scale_concurrent_50_each():
    # 10 concurrent calls with 50 elements each
    import random
    arrays = [random.sample(range(1000), 50) for _ in range(10)]
    sorted_arrays = await asyncio.gather(*[sorter(arr.copy()) for arr in arrays])
    for arr, sorted_arr in zip(arrays, sorted_arrays):
        pass

@pytest.mark.asyncio
async def test_sorter_large_scale_reverse_100():
    # Reverse sorted list of 100 elements
    arr = list(range(99, -1, -1))
    result = await sorter(arr.copy())

# --------------------------
# Throughput Test Cases
# --------------------------

@pytest.mark.asyncio
async def test_sorter_throughput_small_load():
    # Throughput test with small lists (10 concurrent calls)
    arrays = [[i, i-1, i+1] for i in range(10)]
    results = await asyncio.gather(*[sorter(arr.copy()) for arr in arrays])
    for arr, sorted_arr in zip(arrays, results):
        pass

@pytest.mark.asyncio
async def test_sorter_throughput_medium_load():
    # Throughput test with medium-sized lists (20 concurrent calls, 20 elements each)
    import random
    arrays = [random.sample(range(1000), 20) for _ in range(20)]
    results = await asyncio.gather(*[sorter(arr.copy()) for arr in arrays])
    for arr, sorted_arr in zip(arrays, results):
        pass

@pytest.mark.asyncio
async def test_sorter_throughput_high_volume():
    # Throughput test with high volume (50 concurrent calls, 50 elements each)
    import random
    arrays = [random.sample(range(1000), 50) for _ in range(50)]
    results = await asyncio.gather(*[sorter(arr.copy()) for arr in arrays])
    for arr, sorted_arr in zip(arrays, results):
        pass

@pytest.mark.asyncio
async def test_sorter_throughput_varied_sizes():
    # Throughput test with varied sizes (from 1 to 50 elements)
    import random
    arrays = [random.sample(range(1000), size) for size in range(1, 51)]
    results = await asyncio.gather(*[sorter(arr.copy()) for arr in arrays])
    for arr, sorted_arr in zip(arrays, results):
        pass
# 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-mfuy1u0b and push.