⚡️ Speed up function sorter by 57,106%

[codeflash-ai-dev]

📄 57,106% (571.06x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 368 milliseconds → 643 microseconds (best of 698 runs)

📊 Benchmark Performance Details

Benchmark Name	Original Timing	Expected New Timing	Speedup
test_benchmark_bubble_sort.py::test_sort	8.13 milliseconds	55.8 microseconds	14460.67%
test_process_and_sort.py::test_compute_and_sort	19.0 milliseconds	11.0 milliseconds	72.65%
test_process_and_sort.py::test_no_func	8.20 milliseconds	81.4 microseconds	9974.11%

📝 Explanation and details

Certainly! The given program uses bubble sort, which has an average and worst-case time complexity of (O(n^2)). We can optimize this by using a more efficient sorting algorithm like Timsort, which is the default sorting algorithm used in Python's sorted() method and list.sort() method. Timsort has a time complexity of (O(n \log n)).

Here's the modified code.

This code calls the arr.sort() method that sorts the list in place using Timsort, making it significantly faster for larger lists.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 68 Passed
⏪ Replay Tests	✅ 9 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

import random  # used for generating large random lists

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

# unit tests

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

def test_already_sorted_list():
    codeflash_output = sorter([1, 2, 3])
    codeflash_output = sorter([2, 4, 6, 8, 10])

def test_reverse_sorted_list():
    codeflash_output = sorter([3, 2, 1])
    codeflash_output = sorter([10, 8, 6, 4, 2])

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

def test_single_element_list():
    codeflash_output = sorter([1])
    codeflash_output = sorter([42])

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

# Lists with Duplicates
def test_all_elements_the_same():
    codeflash_output = sorter([1, 1, 1])
    codeflash_output = sorter([5, 5, 5, 5])

def test_some_duplicates():
    codeflash_output = sorter([3, 1, 2, 1, 3])
    codeflash_output = sorter([4, 4, 2, 2, 1, 1])

# Lists with Negative Numbers
def test_all_negative_numbers():
    codeflash_output = sorter([-3, -1, -2])
    codeflash_output = sorter([-5, -3, -8, -4, -2])

def test_mixed_positive_and_negative_numbers():
    codeflash_output = sorter([3, -1, 2, -3])
    codeflash_output = sorter([-5, 3, -8, 4, 2])

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

def test_mixed_integers_and_floats():
    codeflash_output = sorter([3, 2.2, 1])
    codeflash_output = sorter([5, 3.3, 8, 4.4, 2])

# Large Scale Test Cases
def test_large_list_of_random_numbers():
    large_list = [random.randint(0, 1000) for _ in range(1000)]
    codeflash_output = sorter(large_list)

def test_large_list_of_sorted_numbers():
    large_list = list(range(1000))
    codeflash_output = sorter(large_list)

def test_large_list_of_reverse_sorted_numbers():
    large_list = list(range(1000, 0, -1))
    codeflash_output = sorter(large_list)

# Performance and Scalability Tests
def test_very_large_list_of_random_numbers():
    large_list = [random.randint(0, 1000) for _ in range(1000)]
    codeflash_output = sorter(large_list)

# Special Cases
def test_list_with_different_data_types():
    with pytest.raises(TypeError):
        sorter([3, 'a', 2])
    with pytest.raises(TypeError):
        sorter([1, 2, '3'])
# 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_sorted_list():
    # Basic functionality: already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter([-5, -3, -1, 0, 2])

def test_reversed_list():
    # Basic functionality: reversed list
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter([10, 9, 8, 7, 6])

def test_unsorted_list():
    # Basic functionality: unsorted list
    codeflash_output = sorter([3, 1, 4, 1, 5, 9])
    codeflash_output = sorter([10, -1, 2, 8, 3])

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

def test_single_element_list():
    # Edge case: single element list
    codeflash_output = sorter([1])
    codeflash_output = sorter([-5])

def test_two_elements_list():
    # Edge case: two elements list
    codeflash_output = sorter([2, 1])
    codeflash_output = sorter([1, 2])

def test_list_with_duplicates():
    # Lists with duplicates: all elements same
    codeflash_output = sorter([1, 1, 1, 1])
    codeflash_output = sorter([0, 0, 0, 0, 0])
    # Lists with duplicates: some elements same
    codeflash_output = sorter([2, 3, 2, 1, 2])
    codeflash_output = sorter([5, 1, 5, 3, 5])

def test_list_with_negative_numbers():
    # Lists with negative numbers: all negative
    codeflash_output = sorter([-1, -2, -3, -4])
    codeflash_output = sorter([-10, -5, -7, -3])
    # Lists with negative numbers: mixed positive and negative
    codeflash_output = sorter([3, -1, 2, -2, 1])
    codeflash_output = sorter([-5, 3, 0, -2, 4])

def test_list_with_floats():
    # Lists with floating point numbers: all floats
    codeflash_output = sorter([1.1, 2.2, 3.3, 4.4])
    codeflash_output = sorter([3.14, 2.71, 1.41, 1.73])
    # Lists with floating point numbers: mixed integers and floats
    codeflash_output = sorter([1, 2.2, 3, 4.4])
    codeflash_output = sorter([5.5, 3, 2.2, 1])

def test_large_list_of_integers():
    # Large scale test cases: large list of integers
    large_list = list(range(1000, 0, -1))
    codeflash_output = sorter(large_list)
    large_list = list(range(10000, 9000, -1))
    codeflash_output = sorter(large_list)

def test_large_list_of_floats():
    # Large scale test cases: large list of floats
    large_list = [float(i) for i in range(1000, 0, -1)]
    codeflash_output = sorter(large_list)
    large_list = [float(i) for i in range(10000, 9000, -1)]
    codeflash_output = sorter(large_list)

def test_very_large_list():
    # Performance and scalability: very large list
    very_large_list = list(range(100000, 99000, -1))
    codeflash_output = sorter(very_large_list)
    very_very_large_list = list(range(1000))
    codeflash_output = sorter(very_very_large_list)

def test_list_with_extreme_values():
    # Special cases: list with extreme values
    codeflash_output = sorter([1, 2, 3, 999999999, -999999999])
    codeflash_output = sorter([2147483647, -2147483648, 0])

def test_list_with_zero():
    # Special cases: list with zero
    codeflash_output = sorter([0, 1, 2, 3])
    codeflash_output = sorter([0, -1, -2, -3])

def test_list_with_strings():
    # Non-numeric lists: strings
    codeflash_output = sorter(["apple", "banana", "cherry"])
    codeflash_output = sorter(["zebra", "elephant", "dog"])

def test_list_with_special_characters():
    # Lists with special characters
    codeflash_output = sorter(["@", "#", "$", "%"])
    codeflash_output = sorter(["!", "?", ".", ","])

def test_mixed_data_types():
    # Non-numeric lists: mixed data types (should raise an error)
    with pytest.raises(TypeError):
        sorter([1, "apple", 2.2])
    with pytest.raises(TypeError):
        sorter([None, 1, "banana"])
# 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-m8q8mgeo and push.