⚡️ Speed up function sorter by 21,100%

[codeflash-ai]

📄 21,100% (211.00x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 435 milliseconds → 2.05 milliseconds (best of 480 runs)

📝 Explanation and details

Here is an optimized version of your program. The original code uses bubble sort, which is extremely inefficient (O(n²)). Instead, we can use Python's built-in sort() method which uses Timsort (O(n log n)), drastically reducing runtime and memory moves.

The sum computation is already optimal, but a generator is unnecessary for a slice of a list.

Here is the optimized code:

Key Improvements:

• Sorting: Replaces native bubble sort with arr.sort(), reducing overall runtime from O(n²) to O(n log n).
• Summing: Uses direct summing of the sliced list, which is more concise and equally efficient for small slices.

All output and return values remain exactly the same.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 19 Passed
🌀 Generated Regression Tests	✅ 47 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_bubble_sort.py::test_sort	9.05ms	41.1μs	✅21899%
test_bubble_sort_conditional.py::test_sort	8.71μs	5.04μs	✅72.7%
test_bubble_sort_import.py::test_sort	9.03ms	42.0μs	✅21406%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	9.05ms	40.8μs	✅22089%
test_bubble_sort_parametrized.py::test_sort_parametrized	5.71ms	41.7μs	✅13614%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	143μs	32.4μs	✅341%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import sys  # for edge case with sys.maxsize

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

# unit tests

# ---------------------------
# 1. Basic Test Cases
# ---------------------------

def test_sorter_sorted_list():
    # Already sorted input
    arr = [1, 2, 3, 4, 5]
    sorted_arr, summed = sorter(arr.copy()) # 8.00μs -> 4.71μs (69.9% faster)

def test_sorter_reverse_sorted():
    # Reverse sorted input
    arr = [5, 4, 3, 2, 1]
    sorted_arr, summed = sorter(arr.copy()) # 8.75μs -> 4.75μs (84.2% faster)

def test_sorter_unsorted_list():
    # Random unsorted input
    arr = [3, 1, 4, 5, 2]
    sorted_arr, summed = sorter(arr.copy()) # 8.42μs -> 4.79μs (75.7% faster)

def test_sorter_with_duplicates():
    # Input with duplicate values
    arr = [2, 3, 2, 1, 3]
    sorted_arr, summed = sorter(arr.copy()) # 8.29μs -> 4.54μs (82.6% faster)

def test_sorter_negative_numbers():
    # Input with negative numbers
    arr = [-1, -3, 2, 0, 1]
    sorted_arr, summed = sorter(arr.copy()) # 8.33μs -> 4.92μs (69.5% faster)

def test_sorter_single_element():
    # Input with a single element
    arr = [42]
    sorted_arr, summed = sorter(arr.copy()) # 6.75μs -> 4.62μs (45.9% faster)

def test_sorter_two_elements():
    # Input with two elements, unsorted
    arr = [9, 2]
    sorted_arr, summed = sorter(arr.copy()) # 6.33μs -> 4.62μs (36.9% faster)

def test_sorter_two_elements_sorted():
    # Input with two elements, already sorted
    arr = [2, 9]
    sorted_arr, summed = sorter(arr.copy()) # 6.96μs -> 4.42μs (57.6% faster)

# ---------------------------
# 2. Edge Test Cases
# ---------------------------

def test_sorter_empty_list():
    # Empty input list
    arr = []
    sorted_arr, summed = sorter(arr.copy()) # 6.17μs -> 4.17μs (48.0% faster)

def test_sorter_all_same_elements():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    sorted_arr, summed = sorter(arr.copy()) # 7.79μs -> 4.83μs (61.2% faster)

def test_sorter_less_than_three_elements():
    # Less than three elements
    arr = [8, 3]
    sorted_arr, summed = sorter(arr.copy()) # 5.58μs -> 4.38μs (27.6% faster)

def test_sorter_exactly_three_elements():
    # Exactly three elements
    arr = [10, -1, 5]
    sorted_arr, summed = sorter(arr.copy()) # 6.04μs -> 4.58μs (31.8% faster)

def test_sorter_with_large_and_small_numbers():
    # Very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize, 0]
    sorted_arr, summed = sorter(arr.copy()) # 7.00μs -> 4.75μs (47.4% faster)

def test_sorter_with_zeros():
    # List with zeros
    arr = [0, 0, 0, 0]
    sorted_arr, summed = sorter(arr.copy()) # 7.54μs -> 4.58μs (64.6% faster)

def test_sorter_with_floats_and_ints():
    # List with both floats and ints
    arr = [1.5, 2, 0.5, 2.5]
    sorted_arr, summed = sorter(arr.copy()) # 10.7μs -> 5.92μs (80.3% faster)

def test_sorter_with_negative_and_positive():
    # List with both negative and positive numbers
    arr = [-2, 3, 0, -1]
    sorted_arr, summed = sorter(arr.copy()) # 8.04μs -> 4.67μs (72.3% faster)

def test_sorter_mutation_of_input():
    # Ensure the input list is mutated (since the function sorts in place)
    arr = [5, 3, 1]
    original_id = id(arr)
    sorter(arr) # 7.50μs -> 4.46μs (68.2% faster)

# ---------------------------
# 3. Large Scale Test Cases
# ---------------------------

def test_sorter_large_random_list():
    # Large random list of 1000 elements
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    sorted_arr, summed = sorter(arr.copy()) # 37.8ms -> 126μs (29728% faster)

def test_sorter_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    sorted_arr, summed = sorter(arr.copy()) # 25.6ms -> 57.0μs (44849% faster)

def test_sorter_large_reverse_sorted_list():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    sorted_arr, summed = sorter(arr.copy()) # 41.4ms -> 57.0μs (72573% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [5] * 1000
    sorted_arr, summed = sorter(arr.copy()) # 25.5ms -> 54.2μs (46816% faster)

def test_sorter_large_negative_numbers():
    # Large list with negative numbers
    arr = [-i for i in range(1000)]
    random.shuffle(arr)
    expected = sorted(arr)
    sorted_arr, summed = sorter(arr.copy()) # 36.4ms -> 119μs (30400% faster)

def test_sorter_large_floats():
    # Large list with floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    sorted_arr, summed = sorter(arr.copy()) # 37.2ms -> 451μs (8134% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import random  # used for generating large-scale random test data
import sys  # used for maxsize in edge cases

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

# unit tests

# -------------------- Basic Test Cases --------------------

def test_sorter_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    sorted_arr, summed = sorter(arr.copy()) # 7.88μs -> 4.67μs (68.7% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    sorted_arr, summed = sorter(arr.copy()) # 8.50μs -> 4.67μs (82.1% faster)

def test_sorter_basic_unsorted():
    # Unsorted list with positive integers
    arr = [3, 1, 4, 2, 5]
    sorted_arr, summed = sorter(arr.copy()) # 6.29μs -> 4.71μs (33.6% faster)

def test_sorter_basic_duplicates():
    # List with duplicate values
    arr = [2, 3, 2, 1, 4]
    sorted_arr, summed = sorter(arr.copy()) # 6.08μs -> 4.62μs (31.5% faster)

def test_sorter_basic_negative_numbers():
    # List with negative and positive numbers
    arr = [-3, 0, 2, -1, 1]
    sorted_arr, summed = sorter(arr.copy()) # 6.38μs -> 4.83μs (31.9% faster)

def test_sorter_basic_floats():
    # List with floats and integers
    arr = [2.5, 1, 3.1, 0.9]
    sorted_arr, summed = sorter(arr.copy()) # 8.21μs -> 5.83μs (40.7% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    sorted_arr, summed = sorter(arr.copy()) # 5.79μs -> 4.50μs (28.7% faster)

def test_sorter_basic_two_elements():
    # List with two elements, unsorted
    arr = [10, 5]
    sorted_arr, summed = sorter(arr.copy()) # 5.46μs -> 4.50μs (21.3% faster)

def test_sorter_basic_three_elements():
    # List with three elements, mixed order
    arr = [7, 2, 5]
    sorted_arr, summed = sorter(arr.copy()) # 7.42μs -> 4.67μs (58.9% faster)

# -------------------- Edge Test Cases --------------------

def test_sorter_edge_empty_list():
    # Empty list
    arr = []
    sorted_arr, summed = sorter(arr.copy()) # 6.25μs -> 4.38μs (42.9% faster)

def test_sorter_edge_all_same():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    sorted_arr, summed = sorter(arr.copy()) # 5.96μs -> 4.71μs (26.6% faster)

def test_sorter_edge_already_sorted_floats():
    # Already sorted floats
    arr = [0.1, 0.2, 0.3]
    sorted_arr, summed = sorter(arr.copy()) # 8.83μs -> 5.71μs (54.7% faster)

def test_sorter_edge_large_negative_numbers():
    # Very large negative numbers
    arr = [-sys.maxsize, -100000, -99999]
    sorted_arr, summed = sorter(arr.copy()) # 8.46μs -> 5.00μs (69.2% faster)

def test_sorter_edge_large_positive_numbers():
    # Very large positive numbers
    arr = [sys.maxsize, 999999, 1000000]
    sorted_arr, summed = sorter(arr.copy()) # 8.38μs -> 5.00μs (67.5% faster)

def test_sorter_edge_mixed_types():
    # List with int and float mix, negative and positive
    arr = [-2, 3.5, 0, -1.1, 2]
    sorted_arr, summed = sorter(arr.copy()) # 10.6μs -> 6.42μs (65.6% faster)

def test_sorter_edge_shorter_than_three():
    # List with fewer than three elements
    arr = [9, 1]
    sorted_arr, summed = sorter(arr.copy()) # 7.21μs -> 4.50μs (60.2% faster)

def test_sorter_edge_length_three():
    # List with exactly three elements
    arr = [10, 5, 7]
    sorted_arr, summed = sorter(arr.copy()) # 7.46μs -> 4.50μs (65.8% faster)

def test_sorter_edge_mutation():
    # Ensure original input is not mutated (should be, as per function)
    arr = [5, 4, 3]
    arr_copy = arr.copy()
    sorter(arr) # 7.46μs -> 4.46μs (67.3% faster)

# -------------------- Large Scale Test Cases --------------------

def test_sorter_large_random():
    # Large random list of 1000 integers
    arr = random.sample(range(-10000, -9000), 1000)
    arr_copy = arr.copy()
    sorted_arr, summed = sorter(arr_copy) # 38.2ms -> 126μs (30014% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [1] * 250 + [10] * 250
    arr_copy = arr.copy()
    sorted_arr, summed = sorter(arr_copy) # 29.9ms -> 55.5μs (53741% faster)

def test_sorter_large_already_sorted():
    # Large already sorted list
    arr = list(range(1000))
    arr_copy = arr.copy()
    sorted_arr, summed = sorter(arr_copy) # 25.7ms -> 58.9μs (43473% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    arr_copy = arr.copy()
    sorted_arr, summed = sorter(arr_copy) # 41.3ms -> 57.9μs (71283% faster)

def test_sorter_large_negative_and_positive():
    # Large list with negative and positive numbers
    arr = list(range(-500, 500))
    arr_copy = arr.copy()
    sorted_arr, summed = sorter(arr_copy) # 25.7ms -> 58.3μs (43951% faster)

def test_sorter_large_floats():
    # Large list with floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    arr_copy = arr.copy()
    sorted_arr, summed = sorter(arr_copy) # 37.1ms -> 458μs (8001% faster)
# 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-md43hxk8 and push.