⚡️ Speed up method Sorter.sort_class by 113,722%

[codeflash-ai]

📄 113,722% (1,137.22x) speedup for Sorter.sort_class in code_to_optimize/bubble_sort_codeflash_trace.py

⏱️ Runtime : 1.04 seconds → 911 microseconds (best of 217 runs)

📝 Explanation and details

The optimization replaces a naive O(n²) bubble sort implementation with Python's built-in arr.sort() method, which uses Timsort - a highly optimized O(n log n) hybrid stable sorting algorithm.

Key changes:

• Removed the nested loop bubble sort implementation (lines with for i in range(len(arr)) and for j in range(len(arr) - 1))
• Replaced the manual element swapping logic with a single arr.sort() call
• Maintained in-place sorting behavior and return value

Why this is faster:

1. Algorithmic complexity: Bubble sort is O(n²) while Timsort is O(n log n), providing exponential speedup for larger datasets
2. Implementation efficiency: Python's built-in sort is implemented in optimized C code vs. interpreted Python loops
3. Adaptive behavior: Timsort performs even better on partially sorted data, explaining the massive speedups on already-sorted test cases

Performance results by test case type:

• Small lists (≤10 elements): 70-216% faster due to reduced function call overhead
• Large sorted/reverse-sorted lists: 676,311-987,901% faster as Timsort detects patterns and optimizes accordingly
• Large random lists: 65,882-71,127% faster from the fundamental algorithmic improvement
• Lists with many duplicates: 96,999-105,372% faster as Timsort handles duplicates efficiently

The optimization maintains identical behavior including in-place mutation, return value, and exception handling for incomparable elements.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 56 Passed
⏪ Replay Tests	✅ 1 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import sys  # used for edge cases with sys.maxsize, etc.

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort_codeflash_trace import Sorter
# function to test
from codeflash.benchmarking.codeflash_trace import codeflash_trace

# unit tests

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

def test_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 4.16μs -> 2.30μs (81.0% faster)

def test_single_element():
    # Test sorting a list with a single element
    arr = [42]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 3.77μs -> 2.21μs (70.5% faster)

def test_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 5.18μs -> 2.49μs (108% faster)

def test_reverse_sorted_list():
    # Test sorting a reverse-sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 5.79μs -> 2.54μs (128% faster)

def test_unsorted_list():
    # Test sorting a typical unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 5.53μs -> 2.61μs (112% faster)

def test_list_with_duplicates():
    # Test sorting a list with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 5.59μs -> 2.52μs (122% faster)

def test_list_with_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 5.78μs -> 2.59μs (123% faster)

def test_list_with_all_equal_elements():
    # Test sorting a list where all elements are equal
    arr = [7, 7, 7, 7, 7]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 4.79μs -> 2.40μs (99.5% faster)

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

def test_list_with_min_and_max_integers():
    # Test sorting a list with extreme integer values
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 1, -1]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 6.44μs -> 2.87μs (124% faster)
    expected = sorted(arr)

def test_list_with_large_and_small_numbers():
    # Test sorting a list with both very large and very small numbers
    arr = [1_000_000, -1_000_000, 0, 999_999, -999_999]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 6.29μs -> 2.58μs (144% faster)
    expected = sorted(arr)

def test_list_with_alternating_high_low():
    # Test sorting a list with alternating high and low values
    arr = [100, -100, 99, -99, 98, -98]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 6.92μs -> 2.57μs (169% faster)
    expected = sorted(arr)

def test_stability_with_duplicates():
    # Sorting should be stable for duplicate values (relative order doesn't change for equal elements)
    # Bubble sort is stable, so this should pass
    arr = [5, 3, 3, 2, 5, 1]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 6.33μs -> 2.58μs (145% faster)
    expected = sorted(arr)

def test_list_with_zeroes_and_negatives():
    # Test sorting a list with zeros and negative numbers
    arr = [0, -1, 0, -2, 2, 1]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 6.06μs -> 2.57μs (136% faster)
    expected = sorted(arr)

def test_list_with_one_negative_among_positives():
    # Test sorting a list with a single negative among positives
    arr = [1, 2, 3, -1, 4, 5]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 5.93μs -> 2.60μs (128% faster)
    expected = sorted(arr)

def test_list_with_floats_and_integers():
    # Test sorting a list with both floats and integers
    arr = [1.5, 2, 0.5, 1, -1.5]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 7.78μs -> 3.32μs (134% faster)
    expected = sorted(arr)

def test_list_with_repeated_pattern():
    # Test sorting a list with a repeated pattern
    arr = [1, 2, 3, 1, 2, 3, 1, 2, 3]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 8.28μs -> 2.62μs (216% faster)
    expected = sorted(arr)

def test_list_with_large_gap_values():
    # Test sorting a list with values that have large gaps between them
    arr = [0, 1000000, -1000000, 500, -500]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 6.36μs -> 2.54μs (151% faster)
    expected = sorted(arr)

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

def test_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 55.5ms -> 8.12μs (683183% faster)

def test_large_reverse_sorted_list():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 85.3ms -> 8.64μs (987377% faster)
    expected = list(range(1000))

def test_large_random_list():
    # Test sorting a large random list
    arr = random.sample(range(-10000, -9000), 1000)
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 75.2ms -> 111μs (67524% faster)
    expected = sorted(arr)

def test_large_list_with_duplicates():
    # Test sorting a large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 69.6ms -> 71.7μs (96999% faster)
    expected = sorted(arr)

def test_large_list_with_negatives_and_positives():
    # Test sorting a large list with both negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 74.4ms -> 109μs (67838% faster)
    expected = sorted(arr)

def test_large_list_all_equal():
    # Test sorting a large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 55.6ms -> 7.80μs (713596% faster)

def test_large_list_alternating_pattern():
    # Test sorting a large list with an alternating pattern
    arr = [i % 2 for i in range(1000)]  # 0,1,0,1,...
    codeflash_output = Sorter.sort_class(arr.copy()); result = codeflash_output # 63.7ms -> 31.3μs (203235% faster)
    expected = sorted(arr)

# ------------------------
# Miscellaneous/Mutation-Resistant Cases
# ------------------------

def test_no_side_effect_on_input():
    # Ensure that the input list is not modified if a copy is passed
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    Sorter.sort_class(arr_copy) # 5.77μs -> 2.51μs (130% faster)

def test_returns_reference_to_input():
    # Ensure that the returned list is the same object as the input (bubble sort is in-place)
    arr = [3, 2, 1]
    codeflash_output = Sorter.sort_class(arr); result = codeflash_output # 4.89μs -> 2.46μs (98.5% faster)

def test_sorting_is_idempotent():
    # Sorting a list twice should yield the same result
    arr = [5, 1, 4, 2, 3]
    codeflash_output = Sorter.sort_class(arr.copy()); once_sorted = codeflash_output # 5.67μs -> 2.62μs (117% faster)
    codeflash_output = Sorter.sort_class(once_sorted.copy()); twice_sorted = codeflash_output # 2.81μs -> 1.08μs (160% faster)

def test_sorting_does_not_raise_for_valid_input():
    # Sorting should not raise exceptions for valid input
    arr = [random.randint(-100, 100) for _ in range(10)]
    try:
        Sorter.sort_class(arr.copy())
    except Exception as e:
        pytest.fail(f"Sorter.sort_class raised an exception for valid input: {e}")
# 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 sys  # used for edge case with max/min integers

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort_codeflash_trace import Sorter
# function to test
from codeflash.benchmarking.codeflash_trace import codeflash_trace

# unit tests

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

def test_sort_empty_list():
    # Sorting an empty list should return an empty list
    codeflash_output = Sorter.sort_class([]) # 3.25μs -> 2.50μs (30.3% faster)

def test_sort_single_element():
    # Sorting a single-element list should return the same single element
    codeflash_output = Sorter.sort_class([42]) # 3.35μs -> 2.35μs (42.6% faster)

def test_sort_already_sorted():
    # Sorting an already sorted list should return the same list
    codeflash_output = Sorter.sort_class([1, 2, 3, 4, 5]) # 4.92μs -> 2.44μs (101% faster)

def test_sort_reverse_sorted():
    # Sorting a reverse sorted list should return a sorted list
    codeflash_output = Sorter.sort_class([5, 4, 3, 2, 1]) # 5.91μs -> 2.55μs (132% faster)

def test_sort_unsorted_list():
    # Sorting a typical unsorted list
    codeflash_output = Sorter.sort_class([3, 1, 4, 5, 2]) # 5.75μs -> 2.56μs (125% faster)

def test_sort_with_duplicates():
    # Sorting a list with duplicate elements
    codeflash_output = Sorter.sort_class([2, 3, 2, 1, 3, 1]) # 6.35μs -> 2.67μs (138% faster)

def test_sort_all_equal():
    # Sorting a list where all elements are the same
    codeflash_output = Sorter.sort_class([7, 7, 7, 7]) # 4.57μs -> 2.38μs (91.8% faster)

def test_sort_negative_numbers():
    # Sorting a list with negative numbers
    codeflash_output = Sorter.sort_class([-3, -1, -2, -5, -4]) # 5.62μs -> 2.60μs (116% faster)

def test_sort_mixed_positive_negative():
    # Sorting a list with both positive and negative numbers
    codeflash_output = Sorter.sort_class([-2, 3, 1, -1, 0]) # 5.42μs -> 2.59μs (109% faster)

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

def test_sort_large_and_small_integers():
    # Sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_class(arr) # 6.72μs -> 2.56μs (163% faster)

def test_sort_with_zeroes():
    # Sorting a list with zeroes in various positions
    arr = [0, 5, 0, -1, 0, 3]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_class(arr) # 6.31μs -> 2.45μs (158% faster)

def test_sort_floats_and_integers():
    # Sorting a list with both floats and integers
    arr = [3.2, 1, 4.5, 2, 0.0, -1.1]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_class(arr) # 8.88μs -> 2.82μs (214% faster)

def test_sort_negative_zero():
    # Sorting a list with both 0 and -0.0 (should be treated as equal in Python)
    arr = [0, -0.0, 0.0, -0, 1, -1]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_class(arr); result = codeflash_output # 7.49μs -> 2.73μs (174% faster)
    # Check that all zeros are together and at the correct position
    zero_indices = [i for i, x in enumerate(result) if x == 0 or x == -0.0 or x == 0.0 or x == -0]

def test_sort_list_with_inf_nan():
    # Sorting a list with inf, -inf, and nan
    arr = [float('inf'), -1, float('-inf'), 0, float('nan'), 2]
    codeflash_output = Sorter.sort_class(arr); result = codeflash_output # 7.71μs -> 3.42μs (126% faster)

def test_sort_list_with_only_nan():
    # Sorting a list with only nan values
    arr = [float('nan'), float('nan')]
    codeflash_output = Sorter.sort_class(arr); result = codeflash_output # 3.82μs -> 2.55μs (50.0% faster)

def test_sort_list_with_none_raises():
    # Sorting a list with None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        Sorter.sort_class(arr) # 5.79μs -> 4.65μs (24.6% faster)

def test_sort_list_with_strings_raises():
    # Sorting a list with both ints and strings should raise TypeError
    arr = [1, 'a', 2]
    with pytest.raises(TypeError):
        Sorter.sort_class(arr) # 5.78μs -> 4.45μs (29.8% faster)

def test_sort_list_with_custom_objects_raises():
    # Sorting a list with custom objects that do not implement comparison should raise TypeError
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        Sorter.sort_class(arr) # 5.43μs -> 4.52μs (20.1% faster)

def test_sort_list_is_not_mutated():
    # Ensure the original input list is not mutated (since we copy it in the function)
    arr = [3, 2, 1]
    arr_copy = list(arr)
    Sorter.sort_class(arr) # 4.90μs -> 2.43μs (102% faster)

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

def test_sort_large_random_list():
    # Sorting a large list of random integers
    arr = [random.randint(-100000, 100000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_class(arr) # 74.5ms -> 104μs (71127% faster)

def test_sort_large_sorted_list():
    # Sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = Sorter.sort_class(arr) # 55.6ms -> 8.22μs (676311% faster)

def test_sort_large_reverse_sorted_list():
    # Sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = Sorter.sort_class(arr) # 85.4ms -> 8.64μs (987901% faster)

def test_sort_large_duplicates():
    # 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.sort_class(arr) # 69.6ms -> 66.0μs (105372% faster)

def test_sort_large_floats():
    # Sorting a large list of floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_class(arr) # 70.6ms -> 107μs (65882% faster)

def test_sort_large_negative_numbers():
    # Sorting a large list of negative numbers
    arr = [random.randint(-100000, -1) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_class(arr) # 74.1ms -> 105μs (70141% faster)

def test_sort_large_all_equal():
    # Sorting a large list where all elements are the same
    arr = [7] * 1000
    expected = [7] * 1000
    codeflash_output = Sorter.sort_class(arr) # 55.4ms -> 7.81μs (708624% faster)

def test_sort_large_alternating():
    # Sorting a large list with alternating high and low values
    arr = [i if i % 2 == 0 else 1000 - i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_class(arr) # 71.9ms -> 40.0μs (179678% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

⏪ Replay Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_pytest_code_to_optimizetestspytest__replay_test_0.py::test_code_to_optimize_bubble_sort_codeflash_trace_Sorter_sort_class	560μs	4.57μs	12158%✅

To edit these changes git checkout codeflash/optimize-Sorter.sort_class-mf3k2img and push.