⚡️ Speed up function sorter by 121%

[codeflash-ai]

📄 121% (1.21x) speedup for sorter in code_to_optimize/bubble_sort_3.py

⏱️ Runtime : 843 milliseconds → 381 milliseconds (best of 19 runs)

📝 Explanation and details

The optimized bubble sort implements two key algorithmic improvements that significantly reduce unnecessary operations:

1. Early Exit Optimization: The code adds a swapped flag to detect when the array becomes sorted during any pass. If no swaps occur in a complete pass, the array is already sorted and the algorithm terminates early. This is especially powerful for already-sorted or nearly-sorted data, as seen in the test results where sorted lists show dramatic speedups (99924% faster for large sorted lists).

2. Reduced Inner Loop Range: The inner loop range changes from range(len(arr) - 1) to range(len(arr) - 1 - i). After each outer loop iteration, the largest remaining element "bubbles up" to its correct position at the end, so there's no need to re-examine those already-sorted tail elements.

Performance Impact: The line profiler shows the inner loop executes ~4.98M times vs ~14M times in the original (64% reduction). While the swapping operations remain identical (same number of swaps needed), the algorithm eliminates millions of redundant comparisons. The speedup is most pronounced on:

• Already sorted data: 99924% faster (exits after first pass)
• Lists with many duplicates: 7645% faster (becomes sorted quickly)
• Mixed random data: 63-76% faster (fewer total comparisons)

These optimizations maintain bubble sort's O(n²) worst-case complexity but achieve O(n) best-case performance when data is already sorted, making it much more practical for real-world scenarios where input data may have some existing order.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 67 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large scale random data

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort_3 import sorter

# unit tests

# --- Basic Test Cases ---

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 823ns -> 803ns (2.49% faster)

def test_sorter_single_element():
    # Test sorting a list with one element
    codeflash_output = sorter([42]) # 1.17μs -> 1.18μs (1.18% slower)

def test_sorter_already_sorted():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 2.94μs -> 1.57μs (87.3% faster)

def test_sorter_reverse_sorted():
    # Test sorting a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 3.34μs -> 2.95μs (13.3% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate values
    codeflash_output = sorter([2, 3, 2, 1, 3, 1]) # 3.65μs -> 3.21μs (13.5% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -7, -2]) # 2.93μs -> 2.79μs (4.84% faster)

def test_sorter_mixed_sign_numbers():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([-2, 3, 0, -1, 2]) # 3.06μs -> 2.52μs (21.5% faster)

def test_sorter_floats():
    # Test sorting a list of floats
    codeflash_output = sorter([2.1, 1.5, 3.3, 0.0]) # 3.25μs -> 2.60μs (25.0% faster)

def test_sorter_mixed_int_float():
    # Test sorting a list with both integers and floats
    codeflash_output = sorter([1, 2.5, 0, -3.2]) # 3.77μs -> 3.12μs (20.9% faster)

def test_sorter_all_equal():
    # Test sorting a list where all elements are equal
    codeflash_output = sorter([7, 7, 7, 7]) # 2.12μs -> 1.34μs (58.8% faster)

# --- Edge Test Cases ---

def test_sorter_large_negative_and_positive():
    # Test sorting a list with very large and very small numbers
    arr = [1e10, -1e10, 0, 1e-10, -1e-10]
    expected = [-1e10, -1e-10, 0, 1e-10, 1e10]
    codeflash_output = sorter(arr) # 4.35μs -> 3.35μs (30.0% faster)

def test_sorter_alternating_high_low():
    # Test sorting a list that alternates between high and low values
    arr = [100, 1, 99, 2, 98, 3]
    expected = [1, 2, 3, 98, 99, 100]
    codeflash_output = sorter(arr) # 3.68μs -> 2.81μs (30.9% faster)

def test_sorter_sorted_with_duplicates():
    # Test sorting a sorted list with duplicates
    arr = [1, 2, 2, 3, 3, 4]
    expected = [1, 2, 2, 3, 3, 4]
    codeflash_output = sorter(arr) # 2.90μs -> 1.43μs (103% faster)

def test_sorter_single_negative():
    # Test sorting a list with a single negative number among positives
    arr = [5, 3, 1, -7]
    expected = [-7, 1, 3, 5]
    codeflash_output = sorter(arr) # 3.08μs -> 2.72μs (13.2% faster)

def test_sorter_extreme_values():
    # Test sorting a list with extreme values, including infinities
    arr = [float('inf'), float('-inf'), 0, 5]
    expected = [float('-inf'), 0, 5, float('inf')]
    codeflash_output = sorter(arr) # 3.44μs -> 2.48μs (38.6% faster)

def test_sorter_nan_values():
    # Test sorting a list containing NaN values (should put NaNs at the end)
    arr = [3, float('nan'), 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 3.29μs -> 2.34μs (40.4% faster)

def test_sorter_mutation():
    # Test that the input list is mutated (in-place sort)
    arr = [3, 1, 2]
    sorter(arr) # 2.35μs -> 1.97μs (19.7% faster)

def test_sorter_all_nan():
    # Test sorting a list of all NaN values
    arr = [float('nan'), float('nan')]
    codeflash_output = sorter(arr); result = codeflash_output # 1.70μs -> 1.30μs (30.7% faster)

def test_sorter_large_number_of_duplicates():
    # Test sorting a list with many duplicates
    arr = [5] * 100
    codeflash_output = sorter(arr) # 300μs -> 3.88μs (7645% faster)

# --- Large Scale Test Cases ---

def test_sorter_large_random_list():
    # Test sorting a large random list of integers
    arr = random.sample(range(-1000, 0), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 61.2ms -> 37.4ms (63.5% faster)

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 47.2ms -> 47.2μs (99924% faster)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 71.3ms -> 51.0ms (39.7% faster)

def test_sorter_large_duplicates_and_unique():
    # Test sorting a large list with many duplicates and some unique values
    arr = [1] * 900 + list(range(100))
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 45.9ms -> 19.4ms (137% faster)

def test_sorter_large_mixed_types():
    # Test sorting a large list with ints and floats
    arr = [float(i) if i % 2 == 0 else i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 68.4ms -> 63.3μs (107956% faster)

def test_sorter_large_negative_positive():
    # Test sorting a large list with negative and positive numbers
    arr = list(range(-500, 500))
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 62.6ms -> 38.3ms (63.4% faster)

def test_sorter_performance():
    # Test that sorting a large list does not take excessive time
    arr = random.sample(range(-1000, 0), 1000)
    expected = sorted(arr)
    import time
    start = time.time()
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 61.5ms -> 41.3ms (48.9% faster)
    duration = time.time() - start
# 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 large scale random test cases

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort_3 import sorter

# unit tests

# --- Basic Test Cases ---

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 1.16μs -> 922ns (25.5% faster)

def test_sorter_single_element():
    # Test sorting a list with a single element
    codeflash_output = sorter([1]) # 1.27μs -> 1.14μs (11.5% faster)

def test_sorter_sorted_list():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 2.62μs -> 1.43μs (84.1% faster)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 3.27μs -> 2.90μs (12.4% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 5, 2]) # 2.92μs -> 2.63μs (11.1% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = sorter([2, 3, 2, 1, 3]) # 3.16μs -> 2.63μs (19.8% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([0, -1, -3, 2, 1]) # 2.95μs -> 2.52μs (17.2% faster)

def test_sorter_mixed_sign_numbers():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([-2, 3, 0, -1, 2]) # 2.97μs -> 2.46μs (20.8% faster)

def test_sorter_all_equal_elements():
    # Test sorting a list where all elements are equal
    codeflash_output = sorter([7, 7, 7, 7]) # 2.17μs -> 1.40μs (55.9% faster)

# --- Edge Test Cases ---

def test_sorter_large_numbers():
    # Test sorting a list with very large integer values
    codeflash_output = sorter([999999999, -999999999, 0, 123456789]) # 2.93μs -> 2.35μs (24.4% faster)

def test_sorter_small_numbers():
    # Test sorting a list with very small (negative) integer values
    codeflash_output = sorter([-1000000000, -1, -999999999]) # 2.31μs -> 2.00μs (16.0% faster)

def test_sorter_min_max_int():
    # Test sorting a list with Python's min and max int values
    import sys
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    expected = [-sys.maxsize-1, 0, sys.maxsize]
    codeflash_output = sorter(arr) # 2.63μs -> 2.21μs (19.1% faster)

def test_sorter_already_sorted_with_duplicates():
    # Test sorting an already sorted list containing duplicates
    codeflash_output = sorter([1, 2, 2, 3, 4, 4, 5]) # 3.41μs -> 1.40μs (144% faster)

def test_sorter_reverse_sorted_with_duplicates():
    # Test sorting a reverse sorted list containing duplicates
    codeflash_output = sorter([5, 4, 4, 3, 2, 2, 1]) # 4.57μs -> 3.71μs (23.1% faster)

def test_sorter_two_elements_sorted():
    # Test sorting a two-element list that is already sorted
    codeflash_output = sorter([1, 2]) # 1.55μs -> 1.25μs (23.6% faster)

def test_sorter_two_elements_unsorted():
    # Test sorting a two-element list that is not sorted
    codeflash_output = sorter([2, 1]) # 2.07μs -> 1.68μs (23.0% faster)

def test_sorter_list_with_zeroes():
    # Test sorting a list with multiple zeroes
    codeflash_output = sorter([0, 0, 0, 1, -1]) # 3.12μs -> 2.71μs (15.0% faster)

def test_sorter_list_with_one_negative():
    # Test sorting a list with one negative number among positives
    codeflash_output = sorter([5, 3, 2, 1, -1]) # 3.24μs -> 2.90μs (11.8% faster)

def test_sorter_list_with_repeated_negatives():
    # Test sorting a list with repeated negative numbers
    codeflash_output = sorter([-2, -2, -1, -3, -3]) # 3.26μs -> 2.67μs (22.1% faster)

# --- Large Scale Test Cases ---

def test_sorter_large_random_list():
    # Test sorting a large random list of 1000 integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr)) # 64.8ms -> 38.6ms (68.0% faster)

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(list(arr)) # 45.7ms -> 45.2μs (101061% faster)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(list(arr)) # 79.9ms -> 48.0ms (66.6% faster)

def test_sorter_large_list_all_equal():
    # Test sorting a large list where all elements are the same
    arr = [7] * 1000
    expected = [7] * 1000
    codeflash_output = sorter(list(arr)) # 47.3ms -> 46.6μs (101497% faster)

def test_sorter_large_list_with_duplicates():
    # Test sorting a large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr)) # 58.4ms -> 32.8ms (78.2% faster)

def test_sorter_large_list_with_negatives_and_positives():
    # Test sorting a large list with both negative and positive numbers
    arr = [random.randint(-5000, 5000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr)) # 65.3ms -> 37.1ms (76.0% faster)

def test_sorter_large_list_with_min_max_int():
    # Test sorting a large list that includes min and max int values
    import sys
    arr = [random.randint(-10000, 10000) for _ in range(998)] + [sys.maxsize, -sys.maxsize-1]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr)) # 63.5ms -> 36.9ms (71.9% faster)

# --- Determinism Test ---

def test_sorter_determinism():
    # Test that multiple calls with the same input produce the same output
    arr = [3, 1, 4, 1, 5, 9, 2, 6]
    expected = sorted(arr)
    for _ in range(5):
        codeflash_output = sorter(list(arr)) # 18.2μs -> 11.8μs (54.4% faster)

# --- In-place Mutation Test ---

def test_sorter_does_not_return_new_list():
    # The sorter function sorts in-place and returns the same list object
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 2.38μs -> 2.30μs (3.17% faster)

# --- Type Robustness Test ---

def test_sorter_type_error_on_non_list():
    # Test that passing a non-list raises an error
    with pytest.raises(TypeError):
        sorter("not a list") # 3.65μs -> 3.39μs (7.45% faster)
    with pytest.raises(TypeError):
        sorter(123) # 1.51μs -> 1.36μs (10.8% faster)
    with pytest.raises(TypeError):
        sorter(None) # 1.42μs -> 951ns (48.8% faster)

# --- Test with floats ---

def test_sorter_with_floats():
    # Test sorting a list with floating point numbers
    arr = [3.1, 2.4, 5.6, 1.0, 3.1]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr)) # 3.59μs -> 2.91μs (23.3% faster)

def test_sorter_with_mixed_int_float():
    # Test sorting a list with both ints and floats
    arr = [3, 2.5, 1, 4.0, 3.5]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr)) # 4.26μs -> 3.49μs (22.2% faster)

# --- Test with bools (since bool is a subclass of int in Python) ---

def test_sorter_with_bools():
    # Test sorting a list with boolean values
    arr = [True, False, True, False]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr)) # 2.89μs -> 2.49μs (16.1% faster)

def test_sorter_with_ints_and_bools():
    # Test sorting a list with ints and bools mixed
    arr = [True, 2, False, 1]
    expected = sorted(arr)
    codeflash_output = sorter(list(arr)) # 2.90μs -> 2.54μs (14.0% faster)

# --- Test with empty and single element edge cases ---

def test_sorter_empty_list_again():
    # Redundant test for empty list to ensure coverage
    arr = []
    codeflash_output = sorter(arr) # 849ns -> 780ns (8.85% faster)

def test_sorter_single_element_again():
    # Redundant test for single element list to ensure coverage
    arr = [42]
    codeflash_output = sorter(arr) # 1.21μs -> 1.15μs (5.66% 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-mfcsokoa and push.