⚡️ Speed up function sorter by 430,280%

[codeflash-ai-dev]

📄 430,280% (4,302.80x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.87 seconds → 898 microseconds (best of 664 runs)

⚡️ This change will improve the performance of the following benchmarks:

Benchmark File :: Function	Original Runtime	Expected New Runtime	Speedup
/Users/alvinryanputra/cf/codeflash/code_to_optimize/tests/pytest/benchmarks/test_benchmark_bubble_sort.py::test_sort	7.99 milliseconds	37.3 microseconds	21290.29%
/Users/alvinryanputra/cf/codeflash/code_to_optimize/tests/pytest/benchmarks/test_process_and_sort.py::test_compute_and_sort	19.1 milliseconds	10.9 milliseconds	74.44%
/Users/alvinryanputra/cf/codeflash/code_to_optimize/tests/pytest/benchmarks/test_process_and_sort.py::test_no_func	8.00 milliseconds	33.5 microseconds	23764.61%

🔻 This change will degrade the performance of the following benchmarks:

{benchmark_info_degraded}

📝 Explanation and details

Certainly! The given program uses a basic bubble sort algorithm with a time complexity of O(n^2). We can optimize the sorting process by using a more efficient sorting algorithm like Timsort, which is Python's built-in sorting algorithm. Timsort has a time complexity of O(n log n).

Here's the rewritten version of the program.

This code will sort the list much faster, especially on larger datasets. The built-in sort() method in Python is highly optimized and will provide better performance than the original bubble sort implementation.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 46 Passed
⏪ Replay Tests	✅ 2 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details

- benchmarks/codeflash_replay_tests/test_benchmark_bubble_sort__replay_test_0.py
- benchmarks/codeflash_replay_tests/test_process_and_sort__replay_test_0.py
- benchmarks/test_benchmark_bubble_sort.py
- test_bubble_sort.py
- test_bubble_sort_conditional.py
- test_bubble_sort_import.py
- test_bubble_sort_in_class.py
- test_bubble_sort_parametrized.py
- test_bubble_sort_parametrized_loop.py

🌀 Generated Regression Tests Details

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

# unit tests

def test_empty_list():
    """Test sorting an empty list"""
    codeflash_output = sorter([])

def test_single_element_list():
    """Test sorting a list with a single element"""
    codeflash_output = sorter([1])
    codeflash_output = sorter([-5])

def test_two_elements_list():
    """Test sorting a list with two elements"""
    codeflash_output = sorter([2, 1])
    codeflash_output = sorter([1, 2])

def test_already_sorted_list():
    """Test sorting an already sorted list"""
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter([-3, -2, -1, 0, 1])

def test_reverse_sorted_list():
    """Test sorting a reverse sorted list"""
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter([3, 2, 1, 0, -1])

def test_all_elements_the_same():
    """Test sorting a list where all elements are the same"""
    codeflash_output = sorter([1, 1, 1, 1, 1])
    codeflash_output = sorter([0, 0, 0, 0])

def test_list_with_negative_numbers():
    """Test sorting a list containing negative numbers"""
    codeflash_output = sorter([-1, -3, -2, 0, 1])
    codeflash_output = sorter([-5, -4, -3, -2, -1])

def test_list_with_duplicates():
    """Test sorting a list with duplicate elements"""
    codeflash_output = sorter([3, 1, 2, 3, 1])
    codeflash_output = sorter([4, 5, 4, 5, 4])

def test_list_with_floating_point_numbers():
    """Test sorting a list with floating point numbers"""
    codeflash_output = sorter([1.1, 2.2, 3.3, 2.2, 1.1])
    codeflash_output = sorter([0.5, -1.1, 3.3, 2.2])

def test_mixed_positive_and_negative_numbers():
    """Test sorting a list with both positive and negative numbers"""
    codeflash_output = sorter([3, -1, 2, -3, 0])
    codeflash_output = sorter([-2, 4, 1, -5, 3])

def test_large_list():
    """Test sorting a large list"""
    codeflash_output = sorter(list(range(1000, 0, -1)))
    codeflash_output = sorter(list(range(1000)))

def test_performance_with_large_random_list():
    """Test performance with a large list of random elements"""
    import random
    random_list = random.sample(range(1000), 1000)
    codeflash_output = sorter(random_list)

def test_list_with_non_integer_numbers():
    """Test sorting a list with non-integer numbers"""
    codeflash_output = sorter([1.5, 2.3, -0.7, 0.0, 3.3])
    codeflash_output = sorter([0.1, 0.01, 0.001, 0.0001])

def test_list_with_large_numbers():
    """Test sorting a list with very large numbers"""
    codeflash_output = sorter([10**10, 10**12, 10**11])
    codeflash_output = sorter([10**15, 10**14, 10**13])
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

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

# unit tests

# Test for an empty list
def test_sorter_empty_list():
    codeflash_output = sorter([])

# Test for a single element list
def test_sorter_single_element():
    codeflash_output = sorter([1])

# Test for a two elements list
def test_sorter_two_elements():
    codeflash_output = sorter([2, 1])
    codeflash_output = sorter([1, 2])

# Test for a list with multiple elements
def test_sorter_multiple_elements():
    codeflash_output = sorter([3, 1, 2])
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter([3, 3, 2, 1, 2])

# Test for a list with negative numbers
def test_sorter_negative_numbers():
    codeflash_output = sorter([-1, -3, -2])
    codeflash_output = sorter([-1, 2, -3, 4])

# Test for a large list
def test_sorter_large_list():
    codeflash_output = sorter(list(range(1000, 0, -1)))
    codeflash_output = sorter(list(range(1000)))

# Test for a list with repeated elements
def test_sorter_repeated_elements():
    codeflash_output = sorter([1, 1, 1, 1])
    codeflash_output = sorter([2, 3, 2, 3, 2, 3])

# Test for a list with large numbers
def test_sorter_large_numbers():
    codeflash_output = sorter([999999999, 1, -999999999])

# Test for a list with floating point numbers
def test_sorter_floating_point_numbers():
    codeflash_output = sorter([1.1, 1.01, 1.001])
    codeflash_output = sorter([3.14, 2.71, 1.61])

# Test for a list with strings
def test_sorter_strings():
    codeflash_output = sorter(['banana', 'apple', 'cherry'])
    codeflash_output = sorter(['a', 'A'])

# Test for a list with mixed data types
def test_sorter_mixed_data_types():
    with pytest.raises(TypeError):
        sorter([1, 'a', 2])
# 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-m8z6vhdi and push.