⚡️ Speed up method Sorter.sort_static by 107,319%

[codeflash-ai]

📄 107,319% (1,073.19x) speedup for Sorter.sort_static in code_to_optimize/bubble_sort_codeflash_trace.py

⏱️ Runtime : 908 milliseconds → 845 microseconds (best of 208 runs)

📝 Explanation and details

The optimization replaces a manual bubble sort implementation with Python's built-in arr.sort() method, delivering a massive 107,319% speedup.

Key Changes:

• Algorithm replacement: Swapped O(n²) bubble sort with Python's built-in Timsort (O(n log n) average case)
• Implementation efficiency: Python's .sort() is implemented in C and highly optimized for real-world data patterns
• Preserved behavior: Maintains in-place sorting and returns the same list object, preserving the original contract

Why This Works:
The original bubble sort performs nested loops with explicit element comparisons and swaps in Python bytecode. For a 1000-element list, this requires ~500,000 comparisons in the worst case. Python's Timsort leverages:

• Native C implementation (orders of magnitude faster than Python loops)
• Adaptive algorithms that perform better on partially sorted data
• Optimized memory access patterns

Performance by Test Case:

• Small lists (2-10 elements): 60-140% faster due to reduced overhead
• Large sorted lists: 696,790% faster - Timsort detects already-sorted sequences
• Large reverse-sorted lists: 1,009,760% faster - Timsort handles this pattern efficiently
• Large random lists: 70,000%+ faster - O(n log n) vs O(n²) complexity advantage

The optimization is particularly effective for larger datasets where the algorithmic complexity difference dominates, while still providing consistent improvements across all test cases.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 57 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 testing with max/min int values

# 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_static_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.25μs -> 2.51μs (110% faster)

def test_sort_static_reverse_sorted_list():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.54μs -> 2.51μs (121% faster)

def test_sort_static_unsorted_list():
    # Unsorted list should be sorted correctly
    arr = [3, 1, 4, 5, 2]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.45μs -> 2.57μs (112% faster)

def test_sort_static_with_duplicates():
    # List with duplicate elements should be sorted with duplicates preserved
    arr = [4, 2, 2, 3, 1, 4]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 6.08μs -> 2.55μs (139% faster)

def test_sort_static_all_equal():
    # List where all elements are the same should remain unchanged
    arr = [7, 7, 7, 7]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 4.36μs -> 2.40μs (81.6% faster)

def test_sort_static_two_elements():
    # Test with two elements, both sorted and unsorted
    codeflash_output = Sorter.sort_static([1, 2]) # 3.69μs -> 2.32μs (59.1% faster)
    codeflash_output = Sorter.sort_static([2, 1]) # 1.68μs -> 858ns (96.2% faster)

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

def test_sort_static_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 2.87μs -> 2.08μs (37.5% faster)

def test_sort_static_single_element():
    # Single element list should return the same list
    arr = [42]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 3.18μs -> 2.21μs (43.5% faster)

def test_sort_static_negative_numbers():
    # List with negative numbers should be sorted correctly
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.24μs -> 2.53μs (107% faster)

def test_sort_static_mixed_positive_negative():
    # List with both positive and negative numbers
    arr = [5, -2, 3, 0, -1]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.35μs -> 2.55μs (110% faster)

def test_sort_static_includes_zero():
    # List containing zeros
    arr = [0, 3, 2, 0, 1]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.41μs -> 2.46μs (119% faster)

def test_sort_static_large_and_small_ints():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 100, -100]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 6.43μs -> 2.88μs (124% faster)

def test_sort_static_already_sorted_with_duplicates():
    # Already sorted list with duplicates
    arr = [1, 1, 2, 2, 3, 3]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.08μs -> 2.41μs (110% faster)

def test_sort_static_floats_and_integers():
    # List with both floats and integers
    arr = [3.5, 2, 4.1, 2.0, 3]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 6.59μs -> 3.20μs (106% faster)

def test_sort_static_already_sorted_descending():
    # List sorted in descending order should be sorted ascendingly
    arr = [10, 9, 8, 7, 6]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.61μs -> 2.44μs (130% faster)

def test_sort_static_mutates_input():
    # The function should mutate the input list as per the implementation
    arr = [3, 1, 2]
    Sorter.sort_static(arr) # 4.30μs -> 2.42μs (77.9% faster)

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

def test_sort_static_large_random_list():
    # Test sorting a large random list of 1000 elements
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 73.7ms -> 104μs (70104% faster)

def test_sort_static_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 55.6ms -> 8.01μs (693854% faster)

def test_sort_static_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 85.3ms -> 8.46μs (1008731% faster)

def test_sort_static_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.sort_static(arr.copy()); result = codeflash_output # 69.7ms -> 66.1μs (105413% faster)

def test_sort_static_large_negative_and_positive():
    # Large list with a mix of negative and positive numbers
    arr = [random.randint(-5000, 5000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 74.1ms -> 105μs (69900% faster)

# ----------------------- Additional Robustness Tests -----------------------

@pytest.mark.parametrize("arr,expected", [
    ([], []),  # empty
    ([1], [1]),  # single element
    ([2, 1], [1, 2]),  # two elements
    ([5, 3, 1, 4, 2], [1, 2, 3, 4, 5]),  # random order
    ([1, 2, 3, 4, 5], [1, 2, 3, 4, 5]),  # already sorted
    ([5, 4, 3, 2, 1], [1, 2, 3, 4, 5]),  # reverse sorted
    ([3, 3, 3], [3, 3, 3]),  # all same
    ([0, -1, 1], [-1, 0, 1]),  # negative, zero, positive
])
def test_sort_static_various_cases(arr, expected):
    # Parametrized test for various small cases
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 37.1μs -> 19.8μs (87.6% faster)

def test_sort_static_does_not_return_new_object():
    # The function should return the same object (input list), not a new list
    arr = [2, 1]
    codeflash_output = Sorter.sort_static(arr); result = codeflash_output # 3.96μs -> 2.46μs (60.9% 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 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

# 1. Basic Test Cases

def test_sort_static_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 4.87μs -> 2.37μs (106% faster)

def test_sort_static_reverse_list():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.70μs -> 2.42μs (136% faster)

def test_sort_static_random_list():
    # Random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.42μs -> 2.49μs (118% faster)

def test_sort_static_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.30μs -> 2.50μs (112% faster)

def test_sort_static_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 4.77μs -> 2.32μs (106% faster)

# 2. Edge Test Cases

def test_sort_static_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 2.82μs -> 2.27μs (24.4% faster)

def test_sort_static_single_element():
    # Single element list should return the same list
    arr = [42]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 3.22μs -> 2.29μs (40.7% faster)

def test_sort_static_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 3.68μs -> 2.38μs (54.7% faster)

def test_sort_static_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 3.93μs -> 2.38μs (65.0% faster)

def test_sort_static_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, -5, -4]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.46μs -> 2.51μs (118% faster)

def test_sort_static_mixed_sign_numbers():
    # List with both negative and positive numbers
    arr = [-2, 3, 0, -1, 2]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.29μs -> 2.52μs (110% faster)

def test_sort_static_large_and_small_integers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 6.60μs -> 2.82μs (134% faster)

def test_sort_static_floats():
    # List with float numbers
    arr = [2.5, 3.1, 1.0, 2.5, -1.2]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 6.02μs -> 2.65μs (127% faster)

def test_sort_static_mixed_int_float():
    # List with both ints and floats
    arr = [3, 1.5, 2, 0.5, 2.0]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 6.71μs -> 3.21μs (109% faster)

def test_sort_static_with_zeroes():
    # List with zeroes and other numbers
    arr = [0, 0, 1, -1, 0]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.68μs -> 2.44μs (133% faster)

def test_sort_static_large_negative_and_positive():
    # List with large negative and positive numbers
    arr = [-1000000, 1000000, 0, -999999, 999999]
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 6.27μs -> 2.52μs (149% faster)

def test_sort_static_stability():
    # Test that the sort is stable for equal elements (should preserve order)
    # Bubble sort is stable, so for objects with same value, order should be preserved
    class Item:
        def __init__(self, value, label):
            self.value = value
            self.label = label
        def __lt__(self, other):
            return self.value < other.value
        def __gt__(self, other):
            return self.value > other.value
        def __eq__(self, other):
            return self.value == other.value and self.label == other.label
        def __repr__(self):
            return f"Item({self.value}, {self.label})"
    a = Item(2, 'a')
    b = Item(1, 'b')
    c = Item(2, 'c')
    arr = [a, b, c]
    # Sort by value, should preserve order of a and c
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 5.90μs -> 3.61μs (63.4% faster)

def test_sort_static_non_comparable_raises():
    # List with non-comparable elements should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        Sorter.sort_static(arr.copy()) # 5.87μs -> 4.76μs (23.3% faster)

# 3. Large Scale Test Cases

def test_sort_static_large_sorted():
    # Large sorted list
    arr = list(range(1000))
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 55.7ms -> 8.00μs (696790% faster)

def test_sort_static_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 85.4ms -> 8.46μs (1009760% faster)

def test_sort_static_large_random():
    # Large random list
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 74.1ms -> 105μs (70324% faster)

def test_sort_static_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.sort_static(arr.copy()); result = codeflash_output # 69.8ms -> 65.9μs (105752% faster)

def test_sort_static_large_negative_positive():
    # Large list with negative and positive numbers
    arr = [random.randint(-1000000, 1000000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 74.4ms -> 106μs (69636% faster)

def test_sort_static_large_floats():
    # Large list with floats
    arr = [random.uniform(-10000.0, 10000.0) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 70.1ms -> 107μs (65404% faster)

def test_sort_static_large_all_equal():
    # Large list with all elements equal
    arr = [42] * 1000
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 55.8ms -> 7.72μs (722362% faster)

def test_sort_static_large_alternating():
    # Large list with alternating small set of values
    arr = [i % 2 for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = Sorter.sort_static(arr.copy()); result = codeflash_output # 63.7ms -> 27.1μs (234714% 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_static	558μs	4.30μs	12877%✅

To edit these changes git checkout codeflash/optimize-Sorter.sort_static-mf3kajyc and push.