⚡️ Speed up function sorter by 354,543%

[codeflash-ai]

📄 354,543% (3,545.43x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.72 seconds → 1.05 millisecond (best of 568 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	8.11 milliseconds	30.7 microseconds	26284.46%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_compute_and_sort	19.1 milliseconds	11.0 milliseconds	73.58%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_no_func	8.00 milliseconds	28.2 microseconds	28310.14%

📝 Explanation and details

The current implementation of sorter is using a bubble sort algorithm, which has a 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 in Python's sort() method and has a time complexity of (O(n \log n)).

This implementation leverages Python's built-in sort() method, which is much faster than the bubble sort, especially for large lists. The functionality and the output of the function remain the same.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 48 Passed
⏪ Replay Tests	✅ 2 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details

- benchmarks/codeflash_replay_tests_2k230zxp/test_code_to_optimize_tests_pytest_benchmarks_test_benchmark_bubble_sort__replay_test_0.py - benchmarks/codeflash_replay_tests_2k230zxp/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 random # used to generate large lists with random elements # imports import pytest # used for our unit tests from code_to_optimize.bubble_sort import sorter # unit tests # Basic Test Cases def test_sorted_list(): # Test already sorted lists codeflash_output = sorter([1, 2, 3, 4, 5]) codeflash_output = sorter([-3, -2, -1, 0, 1]) def test_unsorted_list(): # Test unsorted lists codeflash_output = sorter([5, 4, 3, 2, 1]) codeflash_output = sorter([3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]) def test_list_with_duplicates(): # Test lists with duplicate elements codeflash_output = sorter([2, 3, 2, 1, 3, 1]) codeflash_output = sorter([5, 5, 5, 5, 5]) # Edge Test Cases def test_empty_list(): # Test empty list codeflash_output = sorter([]) def test_single_element_list(): # Test single element lists codeflash_output = sorter([1]) codeflash_output = sorter([-1]) def test_two_element_list(): # Test two element lists codeflash_output = sorter([2, 1]) codeflash_output = sorter([1, 2]) def test_identical_elements_list(): # Test list with all identical elements codeflash_output = sorter([7, 7, 7, 7, 7]) def test_list_with_negative_numbers(): # Test list with negative numbers codeflash_output = sorter([-1, -3, -2, -4, -5]) codeflash_output = sorter([0, -1, -2, -3, -4]) def test_list_with_mixed_numbers(): # Test list with mixed positive and negative numbers codeflash_output = sorter([-1, 3, -2, 4, 0]) codeflash_output = sorter([2, -2, 2, -2, 0]) def test_list_with_floats(): # Test list with floating point numbers codeflash_output = sorter([1.1, 3.3, 2.2, 4.4]) codeflash_output = sorter([-1.1, -3.3, -2.2, -4.4]) # Large Scale Test Cases def test_large_list(): # Test large list with 1000 elements in descending order codeflash_output = sorter(list(range(1000, 0, -1))) # Test large list with 10000 elements in ascending order codeflash_output = sorter(list(range(1000))) # Test large list with 10000 elements in descending order codeflash_output = sorter(list(range(10000, 9000, -1))) def test_large_list_with_random_elements(): # Test large list with 10000 unique random elements random_list = random.sample(range(1000), 1000) codeflash_output = sorter(random_list) # Performance and Scalability Test Cases def test_very_large_list(): # Test very large list with 1,000,000 elements in descending order codeflash_output = sorter(list(range(1000000, 999000, -1))) # Test very large list with 1,000,000 unique random elements random_list = random.sample(range(1000), 1000) codeflash_output = sorter(random_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 code_to_optimize.bubble_sort import sorter # unit tests 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_unsorted_list(): # Test with an unsorted list codeflash_output = sorter([3, 1, 4, 5, 2]) codeflash_output = sorter(['b', 'd', 'a', 'c']) 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_all_identical_elements(): # Test with a list of all identical elements codeflash_output = sorter([2, 2, 2, 2]) codeflash_output = sorter(['x', 'x', 'x']) def test_mixed_integers_and_floats(): # Test with a list of integers and floats codeflash_output = sorter([3, 1.5, 2.2, 4, 5.1]) def test_strings_of_different_lengths(): # Test with a list of strings of different lengths codeflash_output = sorter(['apple', 'banana', 'kiwi', 'cherry']) def test_large_list_of_integers(): # Test with a large list of integers large_list = list(range(1000, 0, -1)) sorted_large_list = list(range(1, 1001)) codeflash_output = sorter(large_list) def test_large_list_of_random_integers(): # Test with a large list of random integers import random random_list = random.sample(range(1, 1001), 1000) codeflash_output = sorter(random_list) def test_negative_numbers(): # Test with a list of negative numbers codeflash_output = sorter([-1, -3, -2, -5, -4]) def test_positive_and_negative_numbers(): # Test with a list of positive and negative numbers codeflash_output = sorter([3, -1, 2, -4, 0]) def test_duplicate_numbers(): # Test with a list of duplicate numbers codeflash_output = sorter([4, 2, 4, 3, 2]) def test_very_large_list(): # Test with a very large list very_large_list = list(range(10000, 9000, -1)) sorted_very_large_list = list(range(1, 1001)) codeflash_output = sorter(very_large_list) def test_list_with_tuples(): # Test with a list of tuples, sorting by the first element codeflash_output = sorter([(2, 'b'), (1, 'a'), (3, 'c')]) def test_list_with_custom_objects(): # Test with a list of custom objects class CustomObject: def __init__(self, value): self.value = value def __lt__(self, other): return self.value < other.value def __eq__(self, other): return self.value == other.value def __repr__(self): return f"CustomObject({self.value})" obj_list = [CustomObject(3), CustomObject(1), CustomObject(2)] sorted_obj_list = [CustomObject(1), CustomObject(2), CustomObject(3)] codeflash_output = sorter(obj_list) def test_min_max_integer_values(): # Test with a list containing minimum and maximum integer values codeflash_output = sorter([2147483647, -2147483648, 0]) def test_list_with_none_values(): # Test with a list containing None values with pytest.raises(TypeError): sorter([3, None, 1, None, 2]) def test_list_with_boolean_values(): # Test with a list containing boolean values codeflash_output = sorter([True, False, True, False]) # 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-m9da5kf9 and push.