⚡️ Speed up method Sorter.sorter by 205,046%

[codeflash-ai]

📄 205,046% (2,050.46x) speedup for Sorter.sorter in code_to_optimize/bubble_sort_codeflash_trace.py

⏱️ Runtime : 856 milliseconds → 417 microseconds (best of 216 runs)

📝 Explanation and details

The optimization replaces a manual bubble sort implementation with Python's built-in sort() method, delivering massive performance gains.

What was optimized:

• Replaced the nested O(n²) bubble sort loops with self.arr.sort(), which uses Timsort (O(n log n) average case)
• Eliminated manual element swapping and redundant array accesses
• Preserved exact behavior: sorts self.arr in-place and returns self.arr * multiplier

Why this is dramatically faster:

1. Algorithmic complexity: Timsort is O(n log n) vs bubble sort's O(n²), making it exponentially faster for larger inputs
2. Native implementation: Python's sort() is implemented in highly optimized C code
3. Reduced overhead: Eliminates Python loop overhead and multiple array index operations per comparison

Test case performance patterns:

• Small arrays (2-4 elements): 70-160% speedup due to reduced Python overhead
• Large arrays (100-1000 elements): 15,000x to 1,000,000x+ speedup due to algorithmic efficiency
• Already sorted data: Massive gains since Timsort has O(n) best-case performance vs bubble sort's O(n²) regardless of input order
• Reverse sorted data: Most dramatic improvements (1,000,000x+) as this is bubble sort's worst case

The optimization maintains identical sorting behavior and return value semantics while delivering the most significant performance improvement for larger datasets where the O(n²) vs O(n log n) complexity difference dominates.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 121 Passed
⏪ Replay Tests	✅ 1 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

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_sorter_basic_sorted():
    # Already sorted list, multiplier=1
    s = Sorter([1, 2, 3, 4])
    codeflash_output = s.sorter(1); result = codeflash_output # 4.63μs -> 2.08μs (122% faster)

def test_sorter_basic_unsorted():
    # Unsorted list, multiplier=1
    s = Sorter([3, 1, 4, 2])
    codeflash_output = s.sorter(1); result = codeflash_output # 5.44μs -> 2.14μs (154% faster)

def test_sorter_basic_with_duplicates():
    # List with duplicates
    s = Sorter([2, 3, 2, 1])
    codeflash_output = s.sorter(1); result = codeflash_output # 5.62μs -> 2.15μs (161% faster)

def test_sorter_basic_multiplier_gt_1():
    # Multiplier > 1
    s = Sorter([3, 2, 1])
    codeflash_output = s.sorter(2); result = codeflash_output # 4.59μs -> 2.08μs (120% faster)

def test_sorter_basic_multiplier_zero():
    # Multiplier = 0
    s = Sorter([5, 4, 6])
    codeflash_output = s.sorter(0); result = codeflash_output # 4.16μs -> 2.01μs (107% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    s = Sorter([-3, -1, -2, 0])
    codeflash_output = s.sorter(1); result = codeflash_output # 4.50μs -> 2.14μs (110% faster)

def test_sorter_basic_single_element():
    # Single element list
    s = Sorter([42])
    codeflash_output = s.sorter(1); result = codeflash_output # 2.61μs -> 1.95μs (33.6% faster)

def test_sorter_basic_empty_list():
    # Empty list
    s = Sorter([])
    codeflash_output = s.sorter(1); result = codeflash_output # 2.22μs -> 1.88μs (18.0% faster)

# Edge Test Cases
def test_sorter_edge_all_identical():
    # All elements identical
    s = Sorter([7, 7, 7, 7])
    codeflash_output = s.sorter(3); result = codeflash_output # 4.11μs -> 2.11μs (94.7% faster)

def test_sorter_edge_reverse_sorted():
    # Reverse sorted list
    s = Sorter([5, 4, 3, 2, 1])
    codeflash_output = s.sorter(1); result = codeflash_output # 6.34μs -> 2.14μs (196% faster)

def test_sorter_edge_large_negative_multiplier():
    # Negative multiplier (should return empty list, as Python repeats list 0 times for negative)
    s = Sorter([1, 2, 3])
    codeflash_output = s.sorter(-2); result = codeflash_output # 3.55μs -> 1.93μs (83.7% faster)

def test_sorter_edge_mixed_types():
    # Mixed types (should raise TypeError in bubble sort comparison)
    s = Sorter([1, 'a', 3])
    with pytest.raises(TypeError):
        s.sorter(1) # 5.35μs -> 4.24μs (26.2% faster)

def test_sorter_edge_float_and_int():
    # List with floats and ints
    s = Sorter([2.5, 3, 1.1, 2])
    codeflash_output = s.sorter(1); result = codeflash_output # 6.50μs -> 2.83μs (130% faster)

def test_sorter_edge_large_multiplier():
    # Large multiplier, but within reasonable bounds
    s = Sorter([2, 1])
    codeflash_output = s.sorter(500); result = codeflash_output # 4.91μs -> 2.88μs (70.2% faster)

def test_sorter_edge_empty_list_multiplier_zero():
    # Empty list with multiplier zero
    s = Sorter([])
    codeflash_output = s.sorter(0); result = codeflash_output # 2.29μs -> 1.86μs (22.9% faster)

def test_sorter_edge_non_integer_multiplier():
    # Non-integer multiplier (should raise TypeError)
    s = Sorter([1, 2, 3])
    with pytest.raises(TypeError):
        s.sorter(2.5) # 5.21μs -> 3.53μs (47.5% faster)

def test_sorter_edge_multiplier_is_string():
    # Multiplier is a string (should raise TypeError)
    s = Sorter([1, 2, 3])
    with pytest.raises(TypeError):
        s.sorter("2") # 4.93μs -> 3.34μs (47.7% faster)

# Large Scale Test Cases
def test_sorter_large_scale_sorted():
    # Large sorted list
    arr = list(range(1000))
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 63.2ms -> 9.14μs (691431% faster)

def test_sorter_large_scale_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 102ms -> 9.35μs (1092679% faster)

def test_sorter_large_scale_random():
    # Large random list
    import random
    arr = list(range(1000))
    random.shuffle(arr)
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 87.6ms -> 110μs (79192% faster)

def test_sorter_large_scale_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [3] * 250 + [7] * 250
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 73.8ms -> 9.16μs (805892% faster)

def test_sorter_large_scale_multiplier():
    # Large list with large multiplier
    arr = [2, 1, 3]
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(333); result = codeflash_output # 4.95μs -> 2.67μs (84.9% faster)

def test_sorter_large_scale_empty():
    # Large scale test with empty list
    s = Sorter([])
    codeflash_output = s.sorter(1000); result = codeflash_output # 2.23μs -> 1.80μs (24.0% faster)

# Edge: Check that sorting is stable (relative order of equal elements is preserved)
def test_sorter_stability():
    # List with tuples, sorting by first element
    arr = [(2, 'b'), (1, 'a'), (2, 'a'), (1, 'b')]
    # Bubble sort is stable, so relative order of (2, 'b') and (2, 'a') should be preserved
    s = Sorter(arr.copy())
    # This will fail because the implementation does not handle custom comparators, but for ints it works
    # So we check with ints only
    arr_ints = [2, 1, 2, 1]
    s2 = Sorter(arr_ints.copy())
    codeflash_output = s2.sorter(1); result = codeflash_output # 4.56μs -> 1.95μs (134% faster)

# Edge: Check input is not mutated after sorting
def test_sorter_input_mutation():
    arr = [3, 2, 1]
    s = Sorter(arr.copy())
    s.sorter(1) # 4.15μs -> 1.97μs (111% faster)

# Edge: Check that output is not the same object as input
def test_sorter_output_is_new_object():
    arr = [3, 2, 1]
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 4.18μs -> 1.97μs (112% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
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_sorter_basic_sorted():
    # Already sorted list, multiplier 1
    s = Sorter([1, 2, 3])
    codeflash_output = s.sorter(1); result = codeflash_output # 3.50μs -> 1.98μs (76.7% faster)

def test_sorter_basic_unsorted():
    # Simple unsorted list, multiplier 1
    s = Sorter([3, 1, 2])
    codeflash_output = s.sorter(1); result = codeflash_output # 4.07μs -> 2.00μs (103% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    s = Sorter([2, 1, 2])
    codeflash_output = s.sorter(1); result = codeflash_output # 3.88μs -> 2.03μs (91.6% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    s = Sorter([-1, -3, 2, 0])
    codeflash_output = s.sorter(1); result = codeflash_output # 4.35μs -> 2.05μs (112% faster)

def test_sorter_basic_mixed_types():
    # List with positive, negative, and zero
    s = Sorter([0, 5, -2, 3])
    codeflash_output = s.sorter(1); result = codeflash_output # 4.46μs -> 2.08μs (114% faster)

def test_sorter_basic_multiplier():
    # Multiplier > 1
    s = Sorter([2, 1])
    codeflash_output = s.sorter(3); result = codeflash_output # 3.54μs -> 2.05μs (72.7% faster)

def test_sorter_basic_multiplier_zero():
    # Multiplier == 0
    s = Sorter([2, 1])
    codeflash_output = s.sorter(0); result = codeflash_output # 3.43μs -> 1.93μs (78.4% faster)

def test_sorter_basic_multiplier_one():
    # Multiplier == 1
    s = Sorter([2, 1])
    codeflash_output = s.sorter(1); result = codeflash_output # 3.41μs -> 2.00μs (70.5% faster)

# --- Edge Test Cases ---

def test_sorter_edge_empty_list():
    # Empty list
    s = Sorter([])
    codeflash_output = s.sorter(1); result = codeflash_output # 2.27μs -> 1.80μs (26.5% faster)

def test_sorter_edge_empty_list_multiplier():
    # Empty list with multiplier > 1
    s = Sorter([])
    codeflash_output = s.sorter(5); result = codeflash_output # 2.19μs -> 1.82μs (20.4% faster)

def test_sorter_edge_single_element():
    # Single element list
    s = Sorter([42])
    codeflash_output = s.sorter(1); result = codeflash_output # 2.65μs -> 1.92μs (38.2% faster)

def test_sorter_edge_single_element_multiplier():
    # Single element list with multiplier > 1
    s = Sorter([42])
    codeflash_output = s.sorter(4); result = codeflash_output # 2.60μs -> 1.90μs (37.2% faster)

def test_sorter_edge_all_identical():
    # All elements identical
    s = Sorter([7, 7, 7, 7])
    codeflash_output = s.sorter(2); result = codeflash_output # 4.11μs -> 2.05μs (101% faster)

def test_sorter_edge_reverse_sorted():
    # Reverse sorted list
    s = Sorter([5, 4, 3, 2, 1])
    codeflash_output = s.sorter(1); result = codeflash_output # 5.57μs -> 2.15μs (159% faster)

def test_sorter_edge_negative_multiplier():
    # Negative multiplier (should behave like Python list * negative)
    s = Sorter([1, 2, 3])
    codeflash_output = s.sorter(-2); result = codeflash_output # 3.57μs -> 1.92μs (85.9% faster)

def test_sorter_edge_zero_and_negative():
    # List with zeros and negatives
    s = Sorter([0, -1, -2, 0])
    codeflash_output = s.sorter(1); result = codeflash_output # 4.59μs -> 2.11μs (117% faster)

def test_sorter_edge_large_negative_numbers():
    # Large negative numbers
    s = Sorter([-1000000, -500, -1000001])
    codeflash_output = s.sorter(1); result = codeflash_output # 4.10μs -> 2.11μs (94.5% faster)

def test_sorter_edge_float_integers():
    # List with floats and integers
    s = Sorter([3.0, 2, 2.5, 1])
    codeflash_output = s.sorter(1); result = codeflash_output # 6.14μs -> 2.72μs (126% faster)

def test_sorter_edge_float_duplicates():
    # List with duplicate floats
    s = Sorter([1.1, 1.1, 1.0])
    codeflash_output = s.sorter(1); result = codeflash_output # 4.19μs -> 2.18μs (92.4% faster)

def test_sorter_edge_large_positive_numbers():
    # Large positive numbers
    s = Sorter([999999, 1000000, 888888])
    codeflash_output = s.sorter(1); result = codeflash_output # 4.10μs -> 2.06μs (98.5% faster)

def test_sorter_edge_already_sorted_large():
    # Already sorted large list
    arr = list(range(100))
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 433μs -> 2.80μs (15422% faster)

def test_sorter_edge_reverse_sorted_large():
    # Reverse sorted large list
    arr = list(range(99, -1, -1))
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 714μs -> 2.83μs (25105% faster)

def test_sorter_edge_mixed_signs():
    # List with mixed signs
    s = Sorter([-10, 0, 10, -20, 20])
    codeflash_output = s.sorter(1); result = codeflash_output # 5.64μs -> 2.19μs (158% faster)

# --- Large Scale Test Cases ---

def test_sorter_large_random():
    # Large random list
    import random
    arr = random.sample(range(-500, 500), 999)  # 999 unique values
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 88.5ms -> 111μs (79120% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [3] * 499
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 85.8ms -> 9.54μs (899904% faster)

def test_sorter_large_all_identical():
    # Large list all identical
    arr = [42] * 1000
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 63.1ms -> 8.07μs (781431% faster)

def test_sorter_large_multiplier():
    # Large sorted list, large multiplier
    arr = list(range(1000))
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(2); result = codeflash_output # 63.4ms -> 9.50μs (667673% faster)

def test_sorter_large_reverse_multiplier():
    # Large reverse sorted list, large multiplier
    arr = list(range(999, -1, -1))
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(3); result = codeflash_output # 102ms -> 10.6μs (960489% faster)
    expected = sorted(arr) * 3

def test_sorter_large_negative_multiplier():
    # Large list, negative multiplier
    arr = list(range(1000))
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(-1); result = codeflash_output # 63.4ms -> 7.40μs (857055% faster)

def test_sorter_large_empty():
    # Large empty list, multiplier > 1
    arr = []
    s = Sorter(arr)
    codeflash_output = s.sorter(5); result = codeflash_output # 2.54μs -> 1.84μs (37.8% faster)

def test_sorter_large_float():
    # Large list with floats
    arr = [float(i) for i in range(1000)]
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 59.6ms -> 9.14μs (652004% faster)

# --- Determinism Test ---

def test_sorter_determinism():
    # Run the same input multiple times, should always get same result
    arr = [4, 2, 3, 1]
    s1 = Sorter(arr.copy())
    s2 = Sorter(arr.copy())
    codeflash_output = s1.sorter(2); result1 = codeflash_output # 5.06μs -> 2.13μs (138% faster)
    codeflash_output = s2.sorter(2); result2 = codeflash_output # 2.89μs -> 1.07μs (170% faster)

# --- Mutation Sensitivity Test ---

def test_sorter_mutation_sensitivity():
    # If the function does not sort, this test should fail
    arr = [2, 3, 1]
    s = Sorter(arr.copy())
    codeflash_output = s.sorter(1); result = codeflash_output # 4.25μs -> 2.07μs (105% 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_sorter	1.52ms	5.51μs	27523%✅

To edit these changes git checkout codeflash/optimize-Sorter.sorter-mf3jf6c5 and push.