⚡️ Speed up function sorter by 190,052%

[codeflash-ai]

📄 190,052% (1,900.52x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 5.81 seconds → 3.06 milliseconds (best of 408 runs)

📝 Explanation and details

The optimization replaces a naive bubble sort implementation with Python's built-in arr.sort() method, delivering dramatic performance improvements.

Key Changes:

• Eliminated nested loops: Removed the O(n²) bubble sort algorithm that performs redundant comparisons and swaps
• Leveraged Timsort: Python's sort() uses Timsort, a highly optimized hybrid stable sorting algorithm that runs in O(n log n) time
• Removed manual swapping: Eliminated the three-line temporary variable swap with optimized internal operations

Why This Is Faster:
The original bubble sort performs ~75M operations for 1000 elements (nested loops with comparisons and swaps), while Timsort performs ~10K operations. The line profiler shows the nested loops consumed 87% of execution time, with 75M hits on the inner loop alone.

Performance Characteristics:

• Small arrays (≤10 elements): 15-65% faster due to reduced overhead
• Large arrays (1000 elements): 40,000-98,000% faster, as bubble sort's O(n²) complexity becomes prohibitive
• Already sorted data: Timsort's adaptive nature provides exceptional performance (98,000%+ speedup)
• String/float data: Maintains strong performance across all data types

The optimization maintains identical behavior (in-place sorting, same output) while transforming an academic algorithm into production-ready code.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 55 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
benchmarks/test_benchmark_bubble_sort.py::test_sort2	11.3ms	28.6μs	✅39239%
test_bubble_sort.py::test_sort	1.44s	253μs	✅567370%
test_bubble_sort_conditional.py::test_sort	7.12μs	4.29μs	✅66.0%
test_bubble_sort_import.py::test_sort	1.44s	253μs	✅567181%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	1.43s	254μs	✅560640%
test_bubble_sort_parametrized.py::test_sort_parametrized	899ms	255μs	✅352122%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	144μs	30.4μs	✅375%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
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_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.12μs -> 4.38μs (62.9% faster)

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.88μs -> 4.21μs (63.4% faster)

def test_sorter_unsorted_list():
    # Typical unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.33μs -> 4.25μs (49.0% faster)

def test_sorter_list_with_duplicates():
    # List with duplicate values
    arr = [4, 2, 5, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.67μs -> 4.29μs (55.3% faster)

def test_sorter_list_with_negative_numbers():
    # List containing negative numbers
    arr = [-3, 1, -2, 5, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 4.38μs (38.1% faster)

def test_sorter_single_element():
    # List with only one element should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 4.00μs (14.6% faster)

def test_sorter_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 4.08μs (21.4% faster)

def test_sorter_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 4.00μs (19.8% faster)

def test_sorter_all_equal_elements():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 4.08μs (37.8% faster)

# ------------------- EDGE TEST CASES ------------------- #

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 3.96μs (7.38% faster)

def test_sorter_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.62μs -> 5.08μs (50.0% faster)

def test_sorter_floats_and_integers():
    # List with floats and integers
    arr = [3.1, 2, 5.5, 1.0, 2.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.50μs -> 5.58μs (52.2% faster)

def test_sorter_negative_and_positive_floats():
    # List with negative and positive floats
    arr = [-1.1, 2.2, 0.0, -3.3, 4.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.17μs -> 5.42μs (32.3% faster)

def test_sorter_strings():
    # List of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.67μs -> 4.50μs (48.2% faster)

def test_sorter_strings_with_case():
    # List of strings with different cases (ASCII order)
    arr = ["Banana", "apple", "Cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.75μs -> 4.46μs (29.0% faster)

def test_sorter_mixed_types_raises():
    # List with mixed types should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.46μs -> 2.75μs (25.8% faster)

def test_sorter_nan_and_inf():
    # List with float('nan'), float('inf'), float('-inf')
    arr = [float('nan'), 1, float('inf'), -1, float('-inf')]
    # Sorting with NaN: NaN is always placed at the end in Python's sort
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.54μs -> 4.96μs (52.1% faster)

def test_sorter_unicode_strings():
    # List of unicode strings
    arr = ["ápple", "apple", "äpple", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.83μs -> 5.00μs (36.7% faster)

def test_sorter_list_with_none_raises():
    # List with None and numbers should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.08μs -> 2.62μs (17.5% faster)

def test_sorter_list_with_bool():
    # List with booleans and integers
    arr = [True, False, 1, 0]
    # In Python, bool is a subclass of int: False==0, True==1
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.21μs -> 4.50μs (38.0% faster)

# ------------------- LARGE SCALE TEST CASES ------------------- #

def test_sorter_large_random_list():
    # Large random list of 1000 integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 46.5ms -> 117μs (39343% faster)

def test_sorter_large_sorted_list():
    # Already sorted list of 1000 elements
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.4ms -> 52.2μs (61969% faster)

def test_sorter_large_reverse_sorted_list():
    # Reverse sorted list of 1000 elements
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.5ms -> 52.2μs (98575% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 41.6ms -> 91.8μs (45199% faster)

def test_sorter_large_strings():
    # Large list of random lowercase strings
    arr = [''.join(random.choices(string.ascii_lowercase, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.8ms -> 136μs (37975% faster)

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 44.5ms -> 408μs (10781% faster)

# ------------------- ADDITIONAL EDGE CASES ------------------- #

def test_sorter_stability():
    # Test that equal elements retain their original order (stability)
    class StableObj:
        def __init__(self, key, original):
            self.key = key
            self.original = original
        def __lt__(self, other):
            return self.key < other.key
        def __gt__(self, other):
            return self.key > other.key
        def __eq__(self, other):
            return self.key == other.key
        def __repr__(self):
            return f"({self.key},{self.original})"
    arr = [StableObj(2, 'a'), StableObj(1, 'b'), StableObj(2, 'c'), StableObj(1, 'd')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.92μs -> 6.00μs (48.6% faster)

def test_sorter_list_with_zeros_and_negatives():
    # List with zeros and negative numbers
    arr = [0, -1, 0, -2, 0, -3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.46μs -> 4.54μs (64.2% faster)

def test_sorter_mutation():
    # Ensure the input list is mutated (in-place sort)
    arr = [3, 2, 1]
    sorter(arr) # 5.67μs -> 4.17μs (36.0% 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 random lists
import string  # used for testing string sorting

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

# unit tests

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

def test_sorter_already_sorted():
    # Test a list that is already sorted
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 4.25μs (31.4% faster)

def test_sorter_reverse_sorted():
    # Test a list sorted in reverse order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 4.17μs (40.0% faster)

def test_sorter_unsorted():
    # Test a typical unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 4.12μs (41.4% faster)

def test_sorter_duplicates():
    # Test a list with duplicate elements
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.88μs -> 4.21μs (39.6% faster)

def test_sorter_negative_numbers():
    # Test a list containing negative numbers
    arr = [-1, -3, 2, 0, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 4.38μs (38.1% faster)

def test_sorter_single_element():
    # Test a single-element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 4.04μs (16.5% faster)

def test_sorter_two_elements_sorted():
    # Test two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 4.08μs (19.4% faster)

def test_sorter_two_elements_unsorted():
    # Test two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 4.08μs (21.4% faster)

def test_sorter_all_equal():
    # Test a list where all elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 4.08μs (36.7% faster)

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

def test_sorter_empty_list():
    # Test an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 3.96μs (7.38% faster)

def test_sorter_large_negative_and_positive():
    # Test with large negative and positive values
    arr = [999999, -999999, 0, 123456, -123456]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.38μs -> 4.50μs (63.9% faster)

def test_sorter_floats():
    # Test with floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0, 4.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.46μs -> 5.42μs (37.7% faster)

def test_sorter_mixed_int_float():
    # Test with a mix of integers and floats
    arr = [3, 1.5, 2, 4.2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.79μs -> 5.17μs (50.8% faster)

def test_sorter_strings():
    # Test with a list of strings
    arr = ['banana', 'apple', 'cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 4.46μs (37.4% faster)

def test_sorter_strings_with_case():
    # Test with strings with different cases
    arr = ['Banana', 'apple', 'Cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.75μs -> 4.33μs (32.7% faster)

def test_sorter_unicode_strings():
    # Test with unicode strings
    arr = ['café', 'apple', 'banana', 'ápple']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.79μs -> 4.92μs (38.2% faster)

def test_sorter_mutation():
    # Test that the original list is mutated (since bubble sort is in-place)
    arr = [2, 1]
    sorter(arr) # 4.92μs -> 4.04μs (21.7% faster)

def test_sorter_large_identical_prefix():
    # Test with many identical elements and one unique at the end
    arr = [0]*999 + [1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.1ms -> 45.0μs (71229% faster)

def test_sorter_large_identical_suffix():
    # Test with one unique at the start and many identical elements
    arr = [1] + [0]*999
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.0ms -> 46.5μs (68670% faster)

def test_sorter_type_error():
    # Test that sorting a list with incomparable types raises TypeError
    arr = [1, 'a', 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 4.25μs -> 2.83μs (50.0% faster)

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

def test_sorter_large_random_list():
    # Test sorting a large list of random integers
    arr = random.sample(range(-1000, 0), 1000)  # 1000 unique ints
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 46.8ms -> 109μs (42569% faster)

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.2ms -> 52.1μs (61792% faster)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.3ms -> 51.9μs (98758% faster)

def test_sorter_large_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(arr.copy()); result = codeflash_output # 41.8ms -> 92.2μs (45225% faster)

def test_sorter_large_strings():
    # Test sorting a large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.0ms -> 135μs (37470% faster)

def test_sorter_large_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 45.2ms -> 407μs (10996% 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-mdryhc8h and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #125 by aseembits93 was closed.