⚡️ Speed up function sorter by 39,930%

[codeflash-ai-dev]

📄 39,930% (399.30x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 180 milliseconds → 448 microseconds (best of 634 runs)

⚡️ This change improved the performance of the following benchmarks:

Benchmark File :: Function	Original Runtime	Expected New Runtime	Speedup
test_benchmark_bubble_sort.py::test_sort	7.96 milliseconds	35.1 microseconds	22586.58%
test_process_and_sort.py::test_compute_and_sort	19.0 milliseconds	10.9 milliseconds	73.78%
test_process_and_sort.py::test_no_func	8.17 milliseconds	30.1 microseconds	26987.44%

🔻 This change degraded the performance of the following benchmarks:

Benchmark File :: Function	Original Runtime	Expected New Runtime	Slowdown
test_sample_benchmark.py::test_sample_function	5.0 milliseconds	7.5 milliseconds	50.00%

📝 Explanation and details

The original code uses a basic bubble sort, which has a time complexity of (O(n^2)). A more efficient sorting algorithm, such as Timsort (used internally by Python's built-in sort function), can improve the performance significantly. Below is the optimized version of the program.

This version leverages Python's built-in sort function, which is highly optimized and runs with an average complexity of (O(n \log n)), making it much faster for larger lists.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 55 Passed
⏪ Replay Tests	✅ 9 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

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

# unit tests

# Basic Test Cases
def test_empty_list():
    codeflash_output = sorter([])

def test_single_element_list():
    codeflash_output = sorter([1])
    codeflash_output = sorter(['a'])

def test_already_sorted_list():
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter(['a', 'b', 'c', 'd'])

def test_reverse_sorted_list():
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter(['d', 'c', 'b', 'a'])

def test_list_with_duplicates():
    codeflash_output = sorter([3, 1, 2, 3, 1])
    codeflash_output = sorter(['b', 'a', 'b', 'a'])

def test_list_with_negative_numbers():
    codeflash_output = sorter([3, -1, 2, -3, 1])
    codeflash_output = sorter([-5, -10, 0, 5, 10])

def test_list_with_mixed_positive_and_negative_numbers():
    codeflash_output = sorter([3, -1, 2, 0, -2, 1])
    codeflash_output = sorter([-3, 3, -2, 2, -1, 1])

def test_list_with_floating_point_numbers():
    codeflash_output = sorter([3.1, 2.2, 1.3])
    codeflash_output = sorter([1.1, -2.2, 3.3, 0.0])

# Edge Test Cases
def test_list_with_mixed_data_types():
    with pytest.raises(TypeError):
        sorter([3, '2', 1])
    with pytest.raises(TypeError):
        sorter([1, 'a', 3.5])

def test_list_with_none_values():
    with pytest.raises(TypeError):
        sorter([1, None, 3])

def test_list_with_identical_elements():
    codeflash_output = sorter([5, 5, 5, 5, 5])

def test_list_with_large_numbers():
    codeflash_output = sorter([10**10, 10**12, 10**11])

# Large Scale Test Cases
def test_large_list():
    import random
    large_list = random.sample(range(1000), 1000)
    codeflash_output = sorter(large_list)


def test_performance_large_list():
    import random
    large_list = random.sample(range(1000), 1000)
    sorted_list = sorted(large_list)
    codeflash_output = sorter(large_list)



import random  # used for generating large scale test cases

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

# unit tests

def test_sorted_list():
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter([10, 20, 30, 40, 50])

def test_reversed_list():
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter([50, 40, 30, 20, 10])

def test_unsorted_list():
    codeflash_output = sorter([3, 1, 4, 5, 2])
    codeflash_output = sorter([20, 10, 40, 30, 50])

def test_empty_list():
    codeflash_output = sorter([])

def test_single_element_list():
    codeflash_output = sorter([1])
    codeflash_output = sorter([99])

def test_two_elements_list():
    codeflash_output = sorter([2, 1])
    codeflash_output = sorter([5, 5])

def test_list_with_duplicates():
    codeflash_output = sorter([1, 1, 1, 1])
    codeflash_output = sorter([5, 5, 5, 5, 5])
    codeflash_output = sorter([3, 1, 2, 3, 1])
    codeflash_output = sorter([10, 20, 10, 30, 20])

def test_list_with_negative_numbers():
    codeflash_output = sorter([-1, -2, -3, -4, -5])
    codeflash_output = sorter([-10, -20, -30, -40, -50])
    codeflash_output = sorter([3, -1, 2, -4, 0])
    codeflash_output = sorter([20, -10, 40, -30, 0])

def test_list_with_floats():
    codeflash_output = sorter([1.1, 2.2, 3.3, 4.4])
    codeflash_output = sorter([10.5, 20.1, 30.6, 40.2])
    codeflash_output = sorter([3.3, -1.1, 2.2, -4.4, 0.0])
    codeflash_output = sorter([20.5, -10.1, 40.6, -30.2, 0.0])

def test_large_scale():
    large_list = list(range(1000, 0, -1))
    codeflash_output = sorter(large_list)
    
    random.seed(0)
    large_random_list = random.sample(range(1000), 1000)
    codeflash_output = sorter(large_random_list)
    
def test_special_values():
    codeflash_output = sorter([0, 1, 2, 3])
    codeflash_output = sorter([3, 0, -1, -2])
    codeflash_output = sorter([999999999, 1, 1000000000])
    codeflash_output = sorter([1, 999999999, 1000000000])
    codeflash_output = sorter([-999999999, -1, -1000000000])
    codeflash_output = sorter([-1, -999999999, -1000000000])

def test_mixed_data_types():
    codeflash_output = sorter([1, 2.2, 3, 4.4])
    codeflash_output = sorter([10.1, 20, 30.3, 40])
# 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-m8qagbmy and push.