⚡️ Speed up function sorter by 439,308%

[codeflash-ai-dev]

📄 439,308% (4,393.08x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.57 seconds → 813 microseconds (best of 566 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	7.87 milliseconds	38.2 microseconds	20500.68%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_compute_and_sort	18.9 milliseconds	11.0 milliseconds	72.16%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_no_func	7.96 milliseconds	59.2 microseconds	13352.72%

📝 Explanation and details

You can optimize this sorting program by using a more efficient algorithm than the bubble sort currently being used. The bubble sort has a time complexity of (O(n^2)), which is not efficient for large datasets. Instead, we can use Python's built-in sort function which implements Timsort, a hybrid sorting algorithm derived from merge sort and insertion sort, with a time complexity of (O(n \log n)).

Here is the optimized program.

This replacement uses Python's highly optimized built-in sort method, which is much faster for larger lists. The return value and functionality remain the same.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 53 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

# 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
def test_unsorted_list():
    codeflash_output = sorter([3, 1, 4, 2, 5])
    codeflash_output = sorter(["b", "d", "a", "c"])

# Test list with duplicates
def test_list_with_duplicates():
    codeflash_output = sorter([3, 1, 2, 3, 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])
    codeflash_output = sorter([3, -1, 4, -2, 0])

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

# Test list with floating point numbers
def test_list_with_floating_point_numbers():
    codeflash_output = sorter([1.1, 3.3, 2.2, 4.4, 0.0])
    codeflash_output = sorter([-1.1, 3.3, -2.2, 4.4, 0.0])

# Test list with mixed data types (if applicable)
def test_list_with_mixed_data_types():
    codeflash_output = sorter([1, 2.2, 3, 4.4, 5])
    codeflash_output = sorter([3, 1.1, 4, 2.2, 5])

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

# Test edge case with identical elements
def test_identical_elements():
    codeflash_output = sorter([1, 1, 1, 1, 1])
    codeflash_output = sorter(["a", "a", "a", "a"])

# Test edge case with minimum and maximum integer values
def test_min_max_integers():
    codeflash_output = sorter([2147483647, -2147483648, 0, 1, -1])
    codeflash_output = sorter([0, 2147483647, -2147483648])

# Test edge case with minimum and maximum floating point values
def test_min_max_floats():
    codeflash_output = sorter([1.7e+308, -1.7e+308, 0.0, 1.0, -1.0])
    codeflash_output = sorter([0.0, 1.7e+308, -1.7e+308])

# Test edge case with non-comparable elements
def test_non_comparable_elements():
    with pytest.raises(TypeError):
        sorter([1, "a", 2])
    with pytest.raises(TypeError):
        sorter([3, None, 4])
# 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 empty list
    codeflash_output = sorter([])

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

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

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

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

def test_list_with_repeated_elements():
    # Test list with repeated elements
    codeflash_output = sorter([3, 1, 2, 3, 1])
    codeflash_output = sorter([5, 5, 5, 5, 5])

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

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

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

def test_list_with_strings():
    # Test list with strings
    codeflash_output = sorter(["apple", "banana", "cherry", "date"])
    codeflash_output = sorter(["zebra", "yak", "xenon", "wolf"])

def test_mixed_data_types():
    # Test list with mixed data types
    with pytest.raises(TypeError):
        sorter([1, "apple", 3.14, None])
    with pytest.raises(TypeError):
        sorter(["string", 42, [1, 2, 3]])

def test_list_with_boolean_values():
    # Test list with boolean values
    codeflash_output = sorter([True, False, True, False])
    codeflash_output = sorter([False, False, True, True])

To edit these changes git checkout codeflash/optimize-sorter-m908uj34 and push.