⚡️ Speed up function sorter by 196,608%

[codeflash-ai]

📄 196,608% (1,966.08x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.90 seconds → 1.98 milliseconds (best of 112 runs)

📝 Explanation and details

The optimization replaces a manual bubble sort implementation with Python's built-in arr.sort() method, delivering a massive 1966x speedup.

Key Changes:

• Eliminated the nested O(n²) bubble sort loops that performed over 113 million iterations in the profiler
• Replaced manual element swapping with Python's highly optimized Timsort algorithm (O(n log n))
• Removed temporary variable assignments and array indexing operations

Why This Creates Such Dramatic Speedup:

1. Algorithmic complexity: Bubble sort's O(n²) vs Timsort's O(n log n) - for 1000 elements, this means ~1M operations vs ~10K operations
2. Implementation efficiency: Python's sort() is implemented in C and uses advanced optimizations like run detection for partially sorted data
3. Memory access patterns: Built-in sort minimizes cache misses compared to the random access pattern of bubble sort

Performance by Test Case Type:

• Small lists (≤10 elements): 7-50% faster - overhead reduction from eliminating loops
• Large lists (1000 elements): 46,000-98,000% faster - the algorithmic advantage dominates
• Already sorted data: 54,000+ % faster - Timsort's adaptive nature vs bubble sort's blind iteration
• Reverse sorted data: 95,000+ % faster - worst case for bubble sort, but Timsort handles efficiently

The optimization maintains identical functionality while leveraging decades of sorting algorithm research built into Python's standard library.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 61 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	7.55ms	21.9μs	34418%✅
test_bubble_sort.py::test_sort	972ms	156μs	620913%✅
test_bubble_sort_conditional.py::test_sort	10.6μs	7.83μs	35.6%✅
test_bubble_sort_import.py::test_sort	986ms	154μs	639940%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	972ms	158μs	612978%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	602ms	158μs	379081%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	134μs	49.5μs	173%✅

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists

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

# unit tests

# ---------------------------
# 1. BASIC TEST CASES
# ---------------------------

def test_sorter_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.29μs (26.6% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.92μs (21.0% faster)

def test_sorter_basic_unsorted():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 9.00μs (16.7% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.33μs (22.0% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.12μs -> 8.04μs (13.5% faster)

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.46μs -> 8.12μs (16.4% faster)

def test_sorter_basic_two_elements_sorted():
    # Two element list, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.54μs -> 7.92μs (7.89% faster)

# ---------------------------
# 2. EDGE TEST CASES
# ---------------------------

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.62μs -> 7.62μs (13.1% faster)

def test_sorter_edge_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 7.96μs (22.0% faster)

def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 8.00μs (29.7% faster)

def test_sorter_edge_mixed_signs():
    # List with both positive and negative numbers
    arr = [3, -1, 0, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 8.00μs (29.7% faster)

def test_sorter_edge_large_and_small():
    # List with very large and very small numbers
    arr = [9999999, -9999999, 0, 123456, -123456]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 8.21μs (39.1% faster)

def test_sorter_edge_floats():
    # List with floats
    arr = [3.1, 2.2, 5.5, 1.0, 4.8]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.9μs -> 8.54μs (51.2% faster)

def test_sorter_edge_mixed_int_float():
    # List with mixed ints and floats
    arr = [2, 3.5, 1, 4.0, 2.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 9.58μs (21.7% faster)

def test_sorter_edge_already_sorted_with_duplicates():
    # Already sorted with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.58μs (22.3% faster)

def test_sorter_edge_min_max_at_ends():
    # Min at end, max at start
    arr = [100, 2, 3, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 9.00μs (17.1% faster)

def test_sorter_edge_large_gap():
    # Large gap between numbers
    arr = [1, 1000, 2, 999, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 8.83μs (17.9% faster)

# ---------------------------
# 3. LARGE SCALE TEST CASES
# ---------------------------

def test_sorter_large_random():
    # Large random list
    arr = random.sample(range(-500, 500), 1000)  # 1000 unique integers
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.2ms -> 67.8μs (47366% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([0, 1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.6ms -> 61.1μs (46706% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.1ms -> 36.5μs (54862% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.6ms -> 35.1μs (98536% faster)

def test_sorter_large_all_same():
    # Large list with all elements the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 21.1ms -> 33.1μs (63673% faster)

# ---------------------------
# 4. MUTATION SENSITIVITY TESTS
# ---------------------------

def test_sorter_mutation_sensitivity():
    # Ensure that changing comparison direction fails the test
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 8.33μs (16.5% faster)

def test_sorter_mutation_duplicates():
    # Ensure that removing handling of duplicates fails
    arr = [2, 2, 1, 1]
    expected = [1, 1, 2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.88μs -> 7.83μs (13.3% faster)

# ---------------------------
# 5. TYPE ERROR TESTS
# ---------------------------


def test_sorter_type_error_mixed_types():
    # List with mixed types should raise TypeError
    arr = [1, "a", 2.0]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 42.5μs -> 40.2μs (5.70% faster)

# ---------------------------
# 6. IMMUTABILITY TEST
# ---------------------------

def test_sorter_does_not_modify_input():
    # Ensure sorter does not modify original input
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    sorter(arr_copy) # 10.5μs -> 7.75μs (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

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

# unit tests

# --- Basic Test Cases ---

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 9.12μs -> 7.67μs (19.0% faster)

def test_sorter_single_element():
    # Test sorting a single-element list
    codeflash_output = sorter([42]) # 8.38μs -> 8.62μs (2.90% slower)

def test_sorter_sorted_list():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 9.12μs -> 8.46μs (7.87% faster)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse-sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 10.5μs -> 8.29μs (26.6% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 2, 5]) # 10.2μs -> 8.25μs (23.2% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicates
    codeflash_output = sorter([2, 3, 2, 1, 1]) # 10.2μs -> 7.92μs (28.4% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -2, 0, 2, 1]) # 10.5μs -> 8.08μs (29.4% faster)

def test_sorter_mixed_signs():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([0, -1, 1, -2, 2]) # 10.0μs -> 8.29μs (20.6% faster)

def test_sorter_all_equal():
    # Test sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7]) # 9.38μs -> 8.75μs (7.14% faster)

def test_sorter_two_elements():
    # Test sorting a two-element list
    codeflash_output = sorter([2, 1]) # 9.21μs -> 7.71μs (19.5% faster)
    codeflash_output = sorter([1, 2]) # 7.54μs -> 7.12μs (5.84% faster)

# --- Edge Test Cases ---

def test_sorter_large_range():
    # Test sorting with large positive and negative numbers
    codeflash_output = sorter([1_000_000, -1_000_000, 0, 999_999, -999_999]) # 11.2μs -> 8.38μs (34.3% faster)

def test_sorter_floats():
    # Test sorting with floating point numbers
    codeflash_output = sorter([3.1, 2.2, 5.5, 1.0]) # 11.3μs -> 8.83μs (28.3% faster)

def test_sorter_mixed_int_float():
    # Test sorting with a mix of ints and floats
    codeflash_output = sorter([1, 2.2, 0, 3.3, 2]) # 10.7μs -> 8.46μs (26.1% faster)

def test_sorter_with_zeroes():
    # Test sorting with zeroes in the list
    codeflash_output = sorter([0, 0, 0, 1, -1]) # 10.4μs -> 8.96μs (15.8% faster)

def test_sorter_minimal_unsorted():
    # Test sorting a minimal unsorted list
    codeflash_output = sorter([2, 1]) # 9.38μs -> 7.75μs (21.0% faster)

def test_sorter_large_duplicates():
    # Test sorting a list with many duplicates
    arr = [5] * 500 + [1] * 500
    expected = [1] * 500 + [5] * 500
    codeflash_output = sorter(arr) # 28.7ms -> 33.0μs (87015% faster)

def test_sorter_alternating_pattern():
    # Test sorting a list with an alternating pattern
    arr = [1, 3, 1, 3, 1, 3]
    expected = [1, 1, 1, 3, 3, 3]
    codeflash_output = sorter(arr) # 10.2μs -> 7.92μs (28.4% faster)

def test_sorter_extreme_values():
    # Test sorting a list with extreme integer values
    arr = [2**31-1, -2**31, 0]
    expected = [-2**31, 0, 2**31-1]
    codeflash_output = sorter(arr) # 10.2μs -> 9.12μs (11.9% faster)

def test_sorter_non_mutation():
    # Ensure the function does not return a new list, but mutates the input
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 8.71μs -> 9.00μs (3.24% slower)

# --- Large Scale Test Cases ---

def test_sorter_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 21.0ms -> 36.6μs (57135% faster)

def test_sorter_large_reverse_sorted():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 34.4ms -> 36.0μs (95339% faster)

def test_sorter_large_random():
    # Test sorting a large random list
    arr = random.sample(range(1000), 1000)  # 1000 unique elements
    expected = sorted(arr)
    codeflash_output = sorter(arr) # 32.4ms -> 66.0μs (48922% faster)

def test_sorter_large_duplicates_and_negatives():
    # Test sorting a large list with duplicates and negatives
    arr = [random.choice([-10, 0, 10, 20, -20]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr) # 28.4ms -> 61.0μs (46395% faster)

def test_sorter_large_all_equal():
    # Test sorting a large list where all elements are the same
    arr = [7] * 1000
    expected = [7] * 1000
    codeflash_output = sorter(arr) # 21.6ms -> 33.9μs (63610% faster)

def test_sorter_large_alternating():
    # Test sorting a large list with an alternating pattern
    arr = [1, 2] * 500
    expected = [1] * 500 + [2] * 500
    codeflash_output = sorter(arr) # 25.3ms -> 52.6μs (47972% faster)

def test_sorter_large_floats():
    # Test sorting a large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr) # 29.4ms -> 293μs (9888% faster)

# --- Determinism Test ---

def test_sorter_determinism():
    # Test that sorting the same list twice yields the same result
    arr = [5, 3, 2, 4, 1]
    codeflash_output = sorter(arr.copy()); result1 = codeflash_output # 11.3μs -> 7.83μs (44.7% faster)
    codeflash_output = sorter(arr.copy()); result2 = codeflash_output # 8.38μs -> 7.21μs (16.2% faster)

# --- Mutation Testing Robustness ---

@pytest.mark.parametrize("arr,expected", [
    ([1, 2, 3], [1, 2, 3]),  # already sorted
    ([3, 2, 1], [1, 2, 3]),  # reverse sorted
    ([2, 1, 3], [1, 2, 3]),  # random order
    ([1, 1, 1], [1, 1, 1]),  # all equal
    ([1, 2, 2, 3], [1, 2, 2, 3]),  # with duplicates
    ([0, -1, 1], [-1, 0, 1]),  # negatives and zero
])
def test_sorter_mutation_cases(arr, expected):
    # Parametrized mutation robustness test
    codeflash_output = sorter(arr) # 54.2μs -> 48.6μs (11.7% 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-mfw16j11 and push.