⚡️ Speed up function sorter by 440,498%

[codeflash-ai-dev]

📄 440,498% (4,404.98x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.55 seconds → 805 microseconds (best of 563 runs)

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

Benchmark File :: Function	Original Runtime	Expected New Runtime	Speedup
code_to_optimize.tests.pytest.benchmarks.test_benchmark_bubble_sort::test_sort	7.73 milliseconds	28.7 microseconds	26788.05%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_compute_and_sort	18.9 milliseconds	11.1 milliseconds	70.38%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_no_func	8.00 milliseconds	137 microseconds	5751.67%

📝 Explanation and details

To optimize the given program, we'll replace the nested loop that implements bubble sort with a more efficient sorting algorithm like Timsort, which is the algorithm used internally by Python's built-in sort() method for lists. Timsort has an average-case and worst-case time complexity of O(n log n), which is much more efficient than bubble sort's O(n^2).

Here's the optimized code.

This version will perform significantly faster for larger input lists, as it uses the highly optimized Timsort algorithm instead of the inefficient bubble sort.

Here is how Timsort performs in terms of complexity.

• Best-case performance: O(n)
• Average-case performance: O(n log n)
• Worst-case performance: O(n log n)

By leveraging Python's built-in sort() method, you not only gain performance improvements but also enhance code simplicity and readability.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 49 Passed
⏪ Replay Tests	✅ 2 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details

- benchmarks/codeflash_replay_tests_5jaqwltg/test_code_to_optimize_tests_pytest_benchmarks_test_benchmark_bubble_sort__replay_test_0.py
- benchmarks/codeflash_replay_tests_5jaqwltg/test_code_to_optimize_tests_pytest_benchmarks_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

# Test empty list
def test_empty_list():
    codeflash_output = sorter([])

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

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

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

# Test unsorted list with unique elements
def test_unsorted_list_unique_elements():
    codeflash_output = sorter([3, 1, 4, 5, 2])
    codeflash_output = sorter(['b', 'd', 'a', 'c'])

# Test list with duplicates
def test_list_with_duplicates():
    codeflash_output = sorter([3, 1, 2, 3, 2, 1])
    codeflash_output = sorter(['b', 'a', 'b', 'a'])

# Test list with negative numbers
def test_list_with_negative_numbers():
    codeflash_output = sorter([-1, -3, -2, 0, 2, 1])
    codeflash_output = sorter([-5, -4, -3, -2, -1])

# Test list with mixed positive and negative numbers
def test_list_with_mixed_positive_negative_numbers():
    codeflash_output = sorter([3, -1, 4, -5, 0, 2])
    codeflash_output = sorter([-2, 4, -1, 3, 0])

# Test list with floating point numbers
def test_list_with_floating_point_numbers():
    codeflash_output = sorter([3.1, 2.4, 1.5, 4.6, 2.2])
    codeflash_output = sorter([-1.1, 0.0, 2.3, -3.4, 1.1])

# Test list with repeated elements
def test_list_with_repeated_elements():
    codeflash_output = sorter([1, 1, 1, 1, 1])
    codeflash_output = sorter(['a', 'a', 'a', 'a'])

# Test large list
def test_large_list():
    large_list = list(range(1000, 0, -1))
    sorted_large_list = list(range(1, 1001))
    codeflash_output = sorter(large_list)

# Test list with non-comparable elements (should raise TypeError)
def test_list_with_non_comparable_elements():
    with pytest.raises(TypeError):
        sorter([1, 'a', 2, 'b'])
    with pytest.raises(TypeError):
        sorter(['a', 1, 'b', 2])
# 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

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(["a"])

def test_already_sorted_list():
    # Test sorting a list that is already sorted
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter(["a", "b", "c", "d"])

def test_reverse_sorted_list():
    # Test sorting a list that is sorted in reverse order
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter(["d", "c", "b", "a"])

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

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

def test_mixed_positive_and_negative_numbers():
    # Test sorting a list with a mix of positive and negative numbers
    codeflash_output = sorter([3, -1, 2, -3, 1, 0])
    codeflash_output = sorter([0, -1, 1, -2, 2, -3, 3])

def test_list_with_floating_point_numbers():
    # Test sorting a list with floating point numbers
    codeflash_output = sorter([3.1, 2.2, 1.3, 0.4, -1.5])
    codeflash_output = sorter([1.1, 1.01, 1.001, 1.0001])

def test_list_with_strings():
    # Test sorting a list with strings
    codeflash_output = sorter(["banana", "apple", "cherry"])
    codeflash_output = sorter(["zebra", "monkey", "aardvark"])

def test_list_with_mixed_data_types():
    # Test sorting a list with mixed data types should raise TypeError
    with pytest.raises(TypeError):
        sorter([1, "a", 2, "b"])
    with pytest.raises(TypeError):
        sorter([3.1, "apple", 2, "banana"])

def test_large_list():
    # Test sorting a large list
    codeflash_output = sorter(list(range(1000, 0, -1)))
    codeflash_output = sorter(list(range(10000, 9000, -1)))

def test_list_with_identical_elements():
    # Test sorting a list with all identical elements
    codeflash_output = sorter([1, 1, 1, 1, 1])
    codeflash_output = sorter(["a", "a", "a", "a"])

def test_list_with_non_comparable_elements():
    # Test sorting a list with non-comparable elements should raise TypeError
    with pytest.raises(TypeError):
        sorter([1, "a", 2.3, None])
    with pytest.raises(TypeError):
        sorter(["apple", 3, {}, []])

def test_list_with_complex_numbers():
    # Test sorting a list with complex numbers should raise TypeError
    with pytest.raises(TypeError):
        sorter([1+2j, 2+3j, 3+4j])
    with pytest.raises(TypeError):
        sorter([3+1j, 1+3j, 2+2j])

To edit these changes git checkout codeflash/optimize-sorter-m90hu4ve and push.