⚡️ Speed up function sorter by 172,663%

[codeflash-ai]

📄 172,663% (1,726.63x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 4.37 seconds → 2.53 milliseconds (best of 322 runs)

📝 Explanation and details

Impact: high
Impact_explanation: Looking at this optimization pull request, I need to assess several key factors:

Performance Analysis

The speedup is absolutely massive - 172,663% improvement on average. The test results show consistent and dramatic improvements across all scenarios:

• Small arrays: 32-170% faster
• Large arrays: 9,453-113,181% faster
• Existing tests show speedups ranging from 190% to 588,974%

Code Quality Assessment

Positive aspects:

• Replaces inefficient O(n²) bubble sort with Python's highly optimized Timsort (O(n log n))
• Uses built-in arr.sort() which is implemented in C for maximum performance
• Maintains identical functionality and API
• Code becomes much cleaner and more maintainable
• Follows Python best practices (use built-ins when available)

Concerns:

• This is essentially changing the algorithm entirely, not just optimizing the existing implementation
• The original bubble sort implementation might have been intentional for educational purposes or specific requirements

Hot Path Analysis

From the calling function details, I can see:

• The function is called from multiple test files with large datasets (5000 elements)
• It's used in computational workflows (compute_and_sort)
• Multiple integration points suggest this is a core utility function

Technical Correctness

• All tests pass with 100% coverage
• The optimization maintains the same behavior (in-place sorting, return value)
• Handles all edge cases correctly (empty lists, single elements, mixed types, etc.)
• TypeError handling is preserved for incomparable types

Trade-off Assessment

Benefits:

• Massive performance improvement
• Cleaner, more maintainable code
• Leverages decades of sorting algorithm research
• Better algorithmic complexity

Potential downsides:

• Changes the fundamental algorithm (though this is generally positive)
• If the original bubble sort was needed for educational/demonstration purposes, this removes that

Final Assessment

This is an exceptional optimization that provides massive performance gains while improving code quality. The fact that it maintains identical functionality while being dramatically faster makes this a clear win. The performance improvements are so substantial (orders of magnitude) that they would be beneficial in virtually any context.

The only scenario where I might hesitate is if this was specifically educational code meant to demonstrate bubble sort algorithm, but even then, the performance benefits are so significant that it would be worth discussing with the developer.

END OF IMPACT EXPLANATION
CALLING CONTEXT

def sort_from_another_file(arr):
    sorted_arr = sorter(arr)
    return sorted_arr

def test_sort():
    input = [5, 4, 3, 2, 1, 0]
    output = sorter(input)
    assert output == [0, 1, 2, 3, 4, 5]

    input = [5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
    output = sorter(input)
    assert output == [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]

    input = list(reversed(range(5000)))
    output = sorter(input)
    assert output == list(range(5000))

def compute_and_sort(arr):
    # Compute pairwise sums average
    pairwise_average = calculate_pairwise_products(arr)

    # Call sorter function
    sorter(arr.copy())

    return pairwise_average

def compute_and_sort(arr):
    # Compute pairwise sums average
    pairwise_average = calculate_pairwise_products(arr)

    # Call sorter function
    sorter(arr.copy())

    return pairwise_average

    def test_sort(self):
        input = [5, 4, 3, 2, 1, 0]
        output = sorter(input)
        self.assertEqual(output, [0, 1, 2, 3, 4, 5])

        input = [5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
        output = sorter(input)
        self.assertEqual(output, [0.0, 1.0, 2.0, 3.0, 4.0, 5.0])

        input = list(reversed(range(5000)))
        output = sorter(input)
        self.assertEqual(output, list(range(5000)))

END OF CALLING CONTEXT

The optimized code replaces the manual bubble sort implementation with Python's built-in arr.sort() method, resulting in a dramatic 172,663% speedup.

Key optimization:

• Algorithm change: Replaced O(n²) bubble sort with Python's highly optimized Timsort algorithm (O(n log n) average case, O(n) best case)
• Implementation efficiency: Python's built-in sort is implemented in C and uses advanced optimizations like adaptive merging, binary insertion sort for small arrays, and galloping mode

Why this leads to massive speedup:

1. Algorithmic complexity: Bubble sort performs O(n²) comparisons and swaps, while Timsort performs O(n log n) operations on average
2. C-level implementation: Built-in sort runs at native C speed rather than Python bytecode interpretation
3. Adaptive optimizations: Timsort recognizes and exploits existing order in data, making it extremely fast for already-sorted or partially-sorted inputs

Performance characteristics by test case:

• Small arrays (≤10 elements): 32-170% faster due to reduced Python overhead
• Large arrays (1000 elements): 9,453-113,181% faster, with the largest gains on reverse-sorted lists where bubble sort performs worst
• Already sorted data: Exceptional performance due to Timsort's O(n) best-case behavior
• All data types: Consistent improvements across integers, floats, strings, and custom objects

The optimization maintains identical functionality while leveraging decades of sorting algorithm research and implementation optimization.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 60 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	9.36ms	19.2μs	48531%✅
test_bubble_sort.py::test_sort	1.05s	186μs	565124%✅
test_bubble_sort_conditional.py::test_sort	5.92μs	2.04μs	190%✅
test_bubble_sort_import.py::test_sort	1.05s	178μs	588974%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	1.06s	182μs	582827%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	674ms	185μs	363825%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	141μs	15.0μs	848%✅

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting edge cases
import sys  # used for maxsize edge case

# 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]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.88μs -> 1.92μs (154% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [5, 3, 1, 4, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.33μs -> 1.92μs (126% faster)

def test_sorter_basic_duplicates():
    # List with duplicate values
    arr = [3, 1, 2, 3, 2]
    expected = [1, 2, 2, 3, 3]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.92μs -> 1.92μs (104% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2]
    expected = [-3, -2, -1, 0, 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.71μs -> 2.12μs (74.5% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 2.71μs -> 1.83μs (47.7% faster)

def test_sorter_basic_two_elements():
    # List with two elements
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 2.75μs -> 1.83μs (50.0% faster)

# 2. Edge Test Cases

def test_sorter_edge_empty_list():
    # Empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr[:]); result = codeflash_output # 2.21μs -> 1.67μs (32.5% faster)

def test_sorter_edge_all_identical():
    # All elements identical
    arr = [7] * 10
    expected = [7] * 10
    codeflash_output = sorter(arr[:]); result = codeflash_output # 5.62μs -> 2.08μs (170% faster)

def test_sorter_edge_reverse_sorted():
    # List sorted in descending order
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.00μs -> 1.88μs (113% faster)

def test_sorter_edge_alternating_high_low():
    # Alternating high/low values
    arr = [1, 100, 2, 99, 3, 98]
    expected = [1, 2, 3, 98, 99, 100]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.42μs -> 2.12μs (108% faster)

def test_sorter_edge_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.50μs -> 2.12μs (64.7% faster)

def test_sorter_edge_mixed_case_strings():
    # Mixed case strings
    arr = ["Banana", "apple", "Cherry"]
    expected = ["Banana", "Cherry", "apple"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.17μs -> 2.08μs (52.0% faster)

def test_sorter_edge_large_numbers():
    # List with very large numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    expected = [-sys.maxsize-1, 0, sys.maxsize]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.83μs -> 2.25μs (70.4% faster)

def test_sorter_edge_floats_and_integers():
    # List with floats and integers
    arr = [1.5, 2, 0.5, 1]
    expected = [0.5, 1, 1.5, 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 5.21μs -> 2.92μs (78.6% faster)

def test_sorter_edge_negative_and_positive():
    # List with both negative and positive numbers
    arr = [-10, 0, 10, -5, 5]
    expected = [-10, -5, 0, 5, 10]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.04μs -> 2.08μs (94.0% faster)

def test_sorter_edge_unicode_strings():
    # List with unicode strings
    arr = ["á", "a", "ä", "b"]
    expected = ["a", "b", "á", "ä"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.62μs -> 2.54μs (82.0% faster)

def test_sorter_edge_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    expected = ["", "", "a", "b"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.67μs -> 2.04μs (79.7% faster)

def test_sorter_edge_boolean_values():
    # List with boolean values
    arr = [True, False, True, False]
    expected = [False, False, True, True]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.62μs -> 2.04μs (77.5% faster)

def test_sorter_edge_sort_is_stable():
    # Stability: equal elements retain their relative order
    class Item:
        def __init__(self, key, value):
            self.key = key
            self.value = value
        def __lt__(self, other):
            return self.key < other.key
        def __eq__(self, other):
            return self.key == other.key and self.value == other.value
        def __repr__(self):
            return f"Item({self.key}, {self.value})"

    arr = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c')]
    expected = [Item(1, 'a'), Item(1, 'c'), Item(2, 'b')]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 5.46μs -> 3.12μs (74.7% faster)

def test_sorter_edge_custom_objects_with_lt():
    # Custom objects with __lt__ defined
    class MyObj:
        def __init__(self, val):
            self.val = val
        def __lt__(self, other):
            return self.val < other.val
        def __eq__(self, other):
            return self.val == other.val
        def __repr__(self):
            return f"MyObj({self.val})"

    arr = [MyObj(3), MyObj(1), MyObj(2)]
    expected = [MyObj(1), MyObj(2), MyObj(3)]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.46μs -> 2.71μs (64.6% faster)

def test_sorter_edge_uncomparable_types():
    # List with uncomparable types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr[:]) # 2.17μs -> 1.54μs (40.6% faster)

# 3. Large Scale Test Cases

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr[:]); result = codeflash_output # 25.2ms -> 35.7μs (70618% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr[:]); result = codeflash_output # 39.2ms -> 35.1μs (111609% faster)

def test_sorter_large_random():
    # Large random list
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 35.0ms -> 87.2μs (40044% 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[:]); result = codeflash_output # 32.2ms -> 69.2μs (46455% faster)

def test_sorter_large_strings():
    # 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[:]); result = codeflash_output # 39.5ms -> 127μs (30780% faster)

def test_sorter_large_negative_and_positive():
    # Large list with negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 34.5ms -> 89.5μs (38418% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 34.2ms -> 357μs (9453% faster)

def test_sorter_large_boolean():
    # Large list of booleans
    arr = [random.choice([True, False]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 33.5ms -> 58.7μs (56974% 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 string sorting tests
import sys  # used for maxsize/minsize edge case

# 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():
    """Test sorting an already sorted list."""
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.42μs -> 1.96μs (74.5% faster)

def test_sorter_reverse_sorted_list():
    """Test sorting a reverse-sorted list."""
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.08μs -> 1.88μs (118% faster)

def test_sorter_unsorted_list():
    """Test sorting a randomly ordered list."""
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.83μs -> 1.92μs (100% faster)

def test_sorter_list_with_duplicates():
    """Test sorting a list with duplicate elements."""
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.54μs -> 2.08μs (118% faster)

def test_sorter_list_with_negative_numbers():
    """Test sorting a list with negative numbers."""
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.08μs -> 2.12μs (92.1% faster)

def test_sorter_list_with_floats():
    """Test sorting a list with float numbers."""
    arr = [3.2, 1.5, 2.8, 1.1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.62μs -> 2.79μs (65.7% faster)

def test_sorter_list_with_single_element():
    """Test sorting a list with a single element."""
    arr = [42]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 2.62μs -> 1.88μs (40.0% faster)

def test_sorter_list_with_two_elements():
    """Test sorting a list with two elements."""
    arr = [2, 1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 2.71μs -> 1.88μs (44.5% faster)

def test_sorter_list_with_identical_elements():
    """Test sorting a list where all elements are identical."""
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.00μs -> 1.92μs (56.5% faster)

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

def test_sorter_empty_list():
    """Test sorting an empty list."""
    arr = []
    codeflash_output = sorter(arr[:]); result = codeflash_output # 2.21μs -> 1.71μs (29.3% faster)

def test_sorter_list_with_large_and_small_numbers():
    """Test sorting a list with very large and very small integers."""
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.67μs -> 2.62μs (77.8% faster)

def test_sorter_list_with_mixed_int_float():
    """Test sorting a list with both integers and floats."""
    arr = [1, 2.2, 3, 0.5, -1.1]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 5.50μs -> 2.96μs (85.9% faster)

def test_sorter_list_with_strings():
    """Test sorting a list of strings."""
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.88μs -> 2.29μs (69.1% faster)

def test_sorter_list_with_empty_strings():
    """Test sorting a list with empty strings and normal strings."""
    arr = ["", "a", "abc", ""]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.88μs -> 2.12μs (82.4% faster)

def test_sorter_list_with_case_sensitive_strings():
    """Test sorting a list with case-sensitive strings."""
    arr = ["apple", "Banana", "banana", "Apple"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.88μs -> 2.12μs (82.4% faster)

def test_sorter_list_with_booleans():
    """Test sorting a list with booleans (should sort as 0/1)."""
    arr = [True, False, True, False]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 3.71μs -> 2.08μs (78.0% faster)

def test_sorter_list_with_none_raises():
    """Test that sorting a list with None and numbers raises TypeError."""
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr[:]) # 2.12μs -> 1.58μs (34.2% faster)

def test_sorter_list_with_incomparable_types_raises():
    """Test that sorting a list with incomparable types raises TypeError."""
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr[:]) # 1.75μs -> 1.29μs (35.4% faster)

def test_sorter_list_with_nested_lists_raises():
    """Test that sorting a list with nested lists raises TypeError."""
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr[:]) # 1.88μs -> 1.29μs (45.2% faster)

def test_sorter_list_with_nan_and_inf():
    """Test sorting a list with float('nan'), float('inf'), and float('-inf')."""
    arr = [float('nan'), float('inf'), float('-inf'), 0]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.25μs -> 2.62μs (61.9% faster)

def test_sorter_list_is_sorted_inplace():
    """Test that the input list is modified in place."""
    arr = [3, 2, 1]
    sorter(arr) # 3.33μs -> 1.96μs (70.2% faster)

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

def test_sorter_large_sorted_list():
    """Test sorting a large already sorted list."""
    arr = list(range(1000))
    codeflash_output = sorter(arr[:]); result = codeflash_output # 25.6ms -> 35.4μs (72321% 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[:]); result = codeflash_output # 39.9ms -> 35.2μs (113181% faster)

def test_sorter_large_random_list():
    """Test sorting a large randomly shuffled list."""
    arr = list(range(1000))
    random.shuffle(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 34.5ms -> 92.9μs (36993% faster)

def test_sorter_large_list_with_duplicates():
    """Test sorting a large list with many duplicate values."""
    arr = [random.choice([0, 1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 33.0ms -> 70.9μs (46529% faster)

def test_sorter_large_list_of_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[:]); result = codeflash_output # 40.2ms -> 126μs (31737% faster)

def test_sorter_large_list_with_negative_and_positive():
    """Test sorting a large list with both negative and positive numbers."""
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 34.3ms -> 86.5μs (39540% faster)

def test_sorter_large_list_with_floats():
    """Test sorting a large list of floats."""
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]); result = codeflash_output # 34.3ms -> 357μs (9508% faster)

# -------------------------------
# Additional Edge/Mutation Cases
# -------------------------------

def test_sorter_returns_reference():
    """Test that the returned list is the same object as the input list (in-place sort)."""
    arr = [9, 8, 7]
    codeflash_output = sorter(arr); result = codeflash_output # 4.25μs -> 2.04μs (108% faster)


def test_sorter_list_with_unicode_strings():
    """Test sorting a list with unicode strings."""
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 5.58μs -> 2.92μs (91.4% faster)

def test_sorter_list_with_empty_and_nonempty():
    """Test sorting a list with empty and non-empty elements."""
    arr = ["", "a", "", "b"]
    codeflash_output = sorter(arr[:]); result = codeflash_output # 4.04μs -> 2.33μs (73.3% 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-mgvhl73z and push.