⚡️ Speed up function sorter by 91%

[codeflash-ai]

📄 91% (0.91x) speedup for sorter in src/bubble_sort.py

⏱️ Runtime : 1.85 second → 969 milliseconds (best of 9 runs)

📝 Explanation and details

Certainly! The current code is an implementation of the Bubble Sort algorithm, which is not very efficient with a time complexity of O(n^2). We can improve the speed significantly by using the built-in sort method in Python, which uses Timsort with an average time complexity of O(n log n).

Here’s the optimized code.

The sorted function returns a new sorted list while maintaining the original list unchanged. If you need to sort the list in place and not return a new list, you can use the sort method on the list itself.

Both methods are much faster and efficient compared to the original Bubble Sort implementation.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 2 Passed
🌀 Generated Regression Tests	✅ 41 Passed
⏪ Replay Tests	✅ 2 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details

- benchmarks/test_benchmark_sort.py
- benchmarks/test_plugin_py__replay_test_0.py
- benchmarks/test_plugin_py__replay_test_1.py
- test_sort.py

🌀 Generated Regression Tests Details

import random  # used for generating large random lists

# imports
import pytest  # used for our unit tests
from src.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])

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

def test_reverse_sorted_list():
    # Test with a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter([10, 9, 8, 7, 6])

def test_unsorted_list():
    # Test with an unsorted list
    codeflash_output = sorter([3, 1, 4, 1, 5, 9])
    codeflash_output = sorter([10, -1, 2, 8, 0])

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

def test_list_with_negative_numbers():
    # Test with a list containing negative numbers
    codeflash_output = sorter([-3, -1, -4, -2, 0])
    codeflash_output = sorter([3, -1, 4, -1, 5, -9])

def test_list_with_mixed_data_types():
    # Test with a list containing mixed data types (integers and floats)
    codeflash_output = sorter([3.5, 2, 4.0, 1.5, 3])
    codeflash_output = sorter([1, 2.2, 3.3, 0.5])

def test_large_list():
    # Test with a large list of random integers
    large_list = random.sample(range(1000), 1000)
    sorted_large_list = sorted(large_list)
    codeflash_output = sorter(large_list)

def test_edge_cases():
    # Test with a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7, 7])
    # Test with a list containing only two elements
    codeflash_output = sorter([2, 1])
    codeflash_output = sorter([1, 2])

def test_performance_and_scalability():
    # Test the function's performance with very large inputs
    very_large_list = random.sample(range(1000), 1000)
    sorted_very_large_list = sorted(very_large_list)
    codeflash_output = sorter(very_large_list)
# 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 src.bubble_sort import sorter

# unit tests

# Basic Functionality
def test_basic_functionality():
    codeflash_output = sorter([3, 2, 1])
    codeflash_output = sorter([5, 3, 8, 4, 2])

# Already Sorted List
def test_already_sorted():
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter([10, 20, 30, 40, 50])

# Reverse Sorted List
def test_reverse_sorted():
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter([100, 50, 20, 10, 5])

# Single Element List
def test_single_element():
    codeflash_output = sorter([1])
    codeflash_output = sorter([42])

# Empty List
def test_empty_list():
    codeflash_output = sorter([])

# List with Duplicate Elements
def test_duplicates():
    codeflash_output = sorter([4, 2, 2, 3, 1])
    codeflash_output = sorter([5, 3, 5, 3, 5])

# List with Negative Numbers
def test_negative_numbers():
    codeflash_output = sorter([3, -1, 2, -5, 0])
    codeflash_output = sorter([-10, -20, -30, -5, -15])

# List with Mixed Positive and Negative Numbers
def test_mixed_numbers():
    codeflash_output = sorter([3, -1, 2, -5, 0, 7, -3])
    codeflash_output = sorter([-10, 20, -30, 5, -15, 25])

# List with Floating Point Numbers
def test_floating_point_numbers():
    codeflash_output = sorter([3.1, 2.2, 1.3])
    codeflash_output = sorter([5.5, 3.3, 8.8, 4.4, 2.2])

# List with Integers and Floating Point Numbers
def test_mixed_integers_floats():
    codeflash_output = sorter([3, 1.1, 2, 4.4, 0])
    codeflash_output = sorter([5.5, 3, 8, 4.4, 2])

# 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)

# List with Repeated Elements
def test_repeated_elements():
    codeflash_output = sorter([1, 1, 1, 1, 1])
    codeflash_output = sorter([5, 5, 5, 5, 5])

# Run the tests
if __name__ == "__main__":
    pytest.main()
# 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-m7to32nz and push.