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📄 357,176% (3,571.76x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.55 seconds 992 microseconds (best of 547 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 7.77 milliseconds 46.3 microseconds 16686.09%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_compute_and_sort 18.2 milliseconds 10.5 milliseconds 73.15%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_no_func 7.73 milliseconds 38.5 microseconds 19982.32%

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

{benchmark_info_degraded}

📝 Explanation and details

To optimize the given sorting algorithm, we can replace the implemented bubble sort with a more efficient sorting algorithm like Timsort, which is the default sorting algorithm in Python's sort method. Timsort has a time complexity of O(n log n) and is generally more efficient than bubble sort, which has a time complexity of O(n^2).

Here's the optimized version of the code.

This simple change will significantly improve the performance of the sorting operation, especially for larger lists.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 20 Passed
🌀 Generated Regression Tests 69 Passed
⏪ Replay Tests 2 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⚙️ Existing Unit Tests Details
- benchmarks/codeflash_replay_tests/test_to_optimize__replay_test_0.py
- benchmarks/codeflash_replay_tests/test_to_optimize__replay_test_1.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 with an empty list
    codeflash_output = sorter([])

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

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

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

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

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

def test_list_with_mixed_positive_and_negative_numbers():
    # Test with a list that has mixed positive and negative numbers
    codeflash_output = sorter([3, -1, 2, -3, 1, 0])
    codeflash_output = sorter([-10, 5, -3, 2, 0, 1])

def test_list_with_floating_point_numbers():
    # Test with a list that has floating point numbers
    codeflash_output = sorter([1.1, 2.2, 3.3, 0.0, -1.1])
    codeflash_output = sorter([3.14, 2.71, 1.61, 0.0])

def test_list_with_strings_of_different_lengths():
    # Test with a list that has strings of different lengths
    codeflash_output = sorter(["apple", "banana", "cherry", "date"])
    codeflash_output = sorter(["a", "abc", "ab", "abcd"])

def test_large_list():
    # Test with a large list to check performance and scalability
    codeflash_output = sorter(list(range(1000)))  # Already sorted
    codeflash_output = sorter(list(range(1000, 0, -1)))  # Reverse sorted
    codeflash_output = sorter(list(range(500)) + list(range(500)))  # Duplicates

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

def test_list_with_special_characters():
    # Test with a list that has special characters
    codeflash_output = sorter(["!", "@", "#", "$", "%"])
    codeflash_output = sorter(["apple", "banana", "cherry", "!"])

def test_list_with_upper_and_lower_case_strings():
    # Test with a list that has a mix of upper and lower case strings
    codeflash_output = sorter(["apple", "Banana", "cherry", "Date"])
    codeflash_output = sorter(["A", "b", "C", "d"])

def test_list_with_none_values():
    # Test with a list that has None values (expected to raise TypeError)
    with pytest.raises(TypeError):
        sorter([None, 1, 2, 3])
    with pytest.raises(TypeError):
        sorter([None, "a", "b", "c"])
# 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

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

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

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

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

def test_negative_numbers():
    codeflash_output = sorter([-1, -3, -2, -5, -4])
    codeflash_output = sorter([0, -1, 1, -2, 2])

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

def test_floats():
    codeflash_output = sorter([1.1, 3.3, 2.2, 5.5, 4.4])
    codeflash_output = sorter([0.1, -0.1, 0.2, -0.2])

def test_mixed_integers_floats():
    codeflash_output = sorter([1, 2.2, 3, 4.4, 5])
    codeflash_output = sorter([-1, -2.2, 0, 2.2, 1])

def test_strings_varying_lengths():
    codeflash_output = sorter(['apple', 'banana', 'cherry', 'date'])
    codeflash_output = sorter(['a', 'abc', 'ab', 'abcd'])

# Large Scale Test Cases
def test_large_list():
    large_list = list(range(10000, 9000, -1))
    codeflash_output = sorter(large_list)

def test_large_floats():
    large_list = [x * 0.1 for x in range(10000, 9000, -1)]
    codeflash_output = sorter(large_list)

# Edge Cases
def test_none_values():
    with pytest.raises(TypeError):
        sorter([1, None, 2, 3])
    with pytest.raises(TypeError):
        sorter([None, 'a', 'b'])

def test_boolean_values():
    codeflash_output = sorter([True, False, True])
    codeflash_output = sorter([False, True, False, True])

def test_special_floats():
    codeflash_output = sorter([1.0, float('inf'), 2.0, float('-inf'), 3.0])
    codeflash_output = sorter([float('nan'), 1.0, 2.0, float('nan')]); sorted_nan_list = codeflash_output

def test_large_small_numbers():
    codeflash_output = sorter([1e308, 1e-308, 1e307, 1e-307])
    codeflash_output = sorter([1e-10, 1e10, 1e-5, 1e5])

def test_special_characters():
    codeflash_output = sorter(['apple', 'banana!', 'cherry#', 'date])
    codeflash_output = sorter(['a@', 'b#', 'c, 'd%'])

def test_empty_strings():
    codeflash_output = sorter(['apple', '', 'banana', ''])
    codeflash_output = sorter(['', 'a', 'b', ''])

def test_very_long_strings():
    codeflash_output = sorter(['a' * 1000, 'b' * 1000, 'c' * 1000])
    codeflash_output = sorter(['apple' * 200, 'banana' * 200, 'cherry' * 200])

def test_immutable_mutable():
    with pytest.raises(TypeError):
        sorter([1, [2, 3], 'a', {'key': 'value'}])
    with pytest.raises(TypeError):
        sorter([(1, 2), {3, 4}, 'b', [5, 6]])

def test_custom_objects():
    class CustomObject:
        def __init__(self, value):
            self.value = value

    with pytest.raises(TypeError):
        sorter([CustomObject(1), CustomObject(2), CustomObject(3)])
    with pytest.raises(TypeError):
        sorter([1, CustomObject(2), 3])
# 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-m9080j7b and push.

Codeflash

To optimize the given sorting algorithm, we can replace the implemented bubble sort with a more efficient sorting algorithm like Timsort, which is the default sorting algorithm in Python's `sort` method. Timsort has a time complexity of O(n log n) and is generally more efficient than bubble sort, which has a time complexity of O(n^2).

Here's the optimized version of the code.



This simple change will significantly improve the performance of the sorting operation, especially for larger lists.
@codeflash-ai-dev codeflash-ai-dev bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Apr 2, 2025
@codeflash-ai-dev codeflash-ai-dev bot requested a review from alvin-r April 2, 2025 17:48
@alvin-r alvin-r closed this Apr 2, 2025
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