⚡️ Speed up function sorter by 80%

[codeflash-ai]

📄 80% (0.80x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.29 seconds → 1.83 seconds (best of 5 runs)

📝 Explanation and details

The optimized bubble sort implementation achieves a 79% speedup through three key algorithmic improvements:

What was optimized:

1. Early termination with swapped flag: Added a swapped boolean to detect when no swaps occur in a pass, allowing early exit from the sorting loop when the array becomes sorted.

2. Reduced comparison range: Changed the inner loop from range(len(arr) - 1) to range(n - 1 - i), eliminating unnecessary comparisons with already-sorted elements at the end of the array.

3. Tuple swap optimization: Replaced the three-line temporary variable swap with Python's tuple unpacking arr[j], arr[j + 1] = arr[j + 1], arr[j], which is more efficient at the bytecode level.

Why this leads to speedup:

• The line profiler shows a dramatic reduction in inner loop iterations from 113M to 55M hits, demonstrating the effectiveness of both optimizations
• Early termination is particularly powerful for already-sorted or partially-sorted data, as seen in test results where already-sorted arrays show 19,987% speedup
• The reduced comparison range eliminates redundant work since bubble sort naturally moves the largest elements to their final positions

Performance impact based on test cases:

• Small arrays (< 10 elements): Minimal impact due to optimization overhead
• Large random arrays (1000 elements): ~60-65% speedup consistently
• Already sorted arrays: Massive speedup (19,000%+) due to early termination after first pass
• Partially sorted data: Significant benefits from early termination

Workload implications:

Based on the function references, this optimization is particularly valuable since sorter() is called in test suites with large arrays (5000 elements) and may be used in processing pipelines. The early termination feature makes it especially efficient for data that's already partially ordered, which is common in real-world datasets.

✅ Correctness verification report:

Test	Status
⏪ Replay Tests	🔘 None Found
⚙️ Existing Unit Tests	✅ 21 Passed
🔎 Concolic Coverage Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 61 Passed
📊 Tests Coverage	100.0%

⚙️ Click to see Existing Unit Tests

Test File::Test Function	Passed	Original ⏱️	Optimized ⏱️	Speedup
benchmarks/test_benchmark_bubble_sort.py::test_sort2	Yes	6.50ms	4.22ms	53.9%✅
test_bubble_sort.py::test_sort	No	833ms	552ms	50.8%✅
test_bubble_sort_conditional.py::test_sort	Yes	31.8μs	30.2μs	5.39%✅
test_bubble_sort_import.py::test_sort	Yes	828ms	557ms	48.6%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	No	804ms	554ms	44.9%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	No	500ms	416μs	120082%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	Yes	238μs	172μs	38.2%✅

Passed Existing Tests:

• code_to_optimize.tests.pytest.test_bubble_sort_in_class::TestSorter.test_sort_in_pytest_class
• code_to_optimize.tests.pytest.test_bubble_sort_in_class::TestSorter.test_sort_in_pytest_class
• code_to_optimize.tests.pytest.test_bubble_sort_in_class::TestSorter.test_sort_in_pytest_class
• code_to_optimize.tests.pytest.benchmarks.test_benchmark_bubble_sort__perfinstrumented::test_sort
• code_to_optimize.tests.pytest.benchmarks.test_benchmark_bubble_sort::test_sort2
• code_to_optimize.tests.pytest.test_bubble_sort_conditional::test_sort
• code_to_optimize.tests.pytest.test_bubble_sort_parametrized::test_sort_parametrized
• code_to_optimize.tests.pytest.test_bubble_sort_parametrized::test_sort_parametrized
• code_to_optimize.tests.pytest.test_bubble_sort_parametrized::test_sort_parametrized
• code_to_optimize.tests.pytest.test_bubble_sort::test_sort
• code_to_optimize.tests.pytest.test_bubble_sort::test_sort
• code_to_optimize.tests.pytest.test_bubble_sort::test_sort
• code_to_optimize.tests.pytest.test_bubble_sort_parametrized_loop::test_sort_loop_parametrized
• code_to_optimize.tests.pytest.test_bubble_sort_parametrized_loop::test_sort_loop_parametrized
• code_to_optimize.tests.pytest.test_bubble_sort_parametrized_loop::test_sort_loop_parametrized
• code_to_optimize.tests.pytest.test_bubble_sort_parametrized_loop::test_sort_loop_parametrized
• code_to_optimize.tests.pytest.test_bubble_sort_parametrized_loop::test_sort_loop_parametrized
• code_to_optimize.tests.pytest.test_bubble_sort_parametrized_loop::test_sort_loop_parametrized
• code_to_optimize.tests.pytest.test_bubble_sort_import::test_sort
• code_to_optimize.tests.pytest.test_bubble_sort_import::test_sort
• code_to_optimize.tests.pytest.test_bubble_sort_import::test_sort

🌀 Click to see Generated Regression Tests

import random  # used for generating large random lists
import string  # used for string-based edge cases
import sys  # for maxsize/minsize edge cases

# 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():
    # Sorting an empty list should return an empty list
    codeflash_output = sorter([])  # 28.1μs -> 34.1μs (17.6% slower)


def test_sorter_single_element():
    # Sorting a single-element list should return the same list
    codeflash_output = sorter([42])  # 29.5μs -> 29.7μs (0.559% slower)


def test_sorter_two_elements_sorted():
    # Already sorted two-element list should remain unchanged
    codeflash_output = sorter([1, 2])  # 27.9μs -> 28.2μs (1.33% slower)


def test_sorter_two_elements_unsorted():
    # Unsorted two-element list should be sorted
    codeflash_output = sorter([2, 1])  # 28.5μs -> 31.5μs (9.64% slower)


def test_sorter_multiple_integers():
    # Typical unsorted list of integers
    codeflash_output = sorter([3, 1, 4, 1, 5, 9, 2, 6, 5])  # 27.8μs -> 34.0μs (18.2% slower)


def test_sorter_already_sorted():
    # Already sorted list should remain unchanged
    codeflash_output = sorter([1, 2, 3, 4, 5])  # 27.8μs -> 29.5μs (5.78% slower)


def test_sorter_reverse_sorted():
    # Reverse sorted list should be sorted in ascending order
    codeflash_output = sorter([5, 4, 3, 2, 1])  # 31.4μs -> 31.2μs (0.802% faster)


def test_sorter_duplicates():
    # List with duplicate values should be sorted with duplicates adjacent
    codeflash_output = sorter([4, 2, 2, 8, 3, 3, 1])  # 31.1μs -> 31.8μs (2.09% slower)


def test_sorter_negative_numbers():
    # List with negative numbers should be sorted correctly
    codeflash_output = sorter([-3, -1, -4, -2, 0])  # 29.0μs -> 29.1μs (0.429% slower)


def test_sorter_mixed_sign_numbers():
    # List with both negative and positive numbers
    codeflash_output = sorter([0, -1, 3, -2, 2, -3, 1])  # 28.8μs -> 29.8μs (3.08% slower)


def test_sorter_floats():
    # List of floats should be sorted correctly
    codeflash_output = sorter([3.2, 1.5, 4.8, 1.1])  # 34.6μs -> 29.1μs (18.9% faster)


def test_sorter_mixed_ints_and_floats():
    # List with both integers and floats
    codeflash_output = sorter([3, 1.5, 2, 4.8, 1])  # 33.8μs -> 28.9μs (16.9% faster)


# ------------------ EDGE TEST CASES ------------------


def test_sorter_all_identical_elements():
    # All elements are the same
    codeflash_output = sorter([7, 7, 7, 7])  # 30.1μs -> 27.3μs (10.2% faster)


def test_sorter_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999, -999999]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 28.9μs -> 28.0μs (2.97% faster)


def test_sorter_strings():
    # List of strings should be sorted lexicographically
    arr = ["banana", "apple", "cherry"]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 27.6μs -> 27.1μs (1.84% faster)


def test_sorter_empty_strings():
    # List with empty strings
    arr = ["", "a", "abc", ""]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 27.1μs -> 27.5μs (1.36% slower)


def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 29.5μs -> 30.0μs (1.53% slower)


def test_sorter_case_sensitivity():
    # List with mixed-case strings
    arr = ["a", "B", "c", "A", "b", "C"]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 29.5μs -> 28.8μs (2.61% faster)


def test_sorter_single_characters():
    # List with single characters
    arr = list("dcba")
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 29.0μs -> 31.7μs (8.29% slower)


def test_sorter_nested_lists_raises():
    # List with nested lists should raise TypeError
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr)  # 36.8μs -> 38.2μs (3.92% slower)


def test_sorter_heterogeneous_types_raises():
    # List with mixed types that cannot be compared should raise TypeError
    arr = [1, "a", 2.5]
    with pytest.raises(TypeError):
        sorter(arr)  # 40.3μs -> 40.5μs (0.410% slower)


def test_sorter_boolean_values():
    # List with boolean values (False < True)
    arr = [True, False, True, False]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 29.0μs -> 30.2μs (4.00% slower)


def test_sorter_none_raises():
    # List containing None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr)  # 37.4μs -> 36.5μs (2.51% faster)


def test_sorter_large_negative_and_positive_floats():
    # List with large positive and negative floats
    arr = [1e308, -1e308, 0.0, 1e-308, -1e-308]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 32.2μs -> 35.2μs (8.52% slower)


def test_sorter_long_strings():
    # List with very long strings
    arr = ["a" * 100, "b" * 99, "c" * 101]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 28.4μs -> 32.5μs (12.8% slower)


# ------------------ LARGE SCALE TEST CASES ------------------


def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 25.4ms -> 15.6ms (62.8% faster)


def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    expected = arr[:]
    codeflash_output = sorter(arr[:])  # 17.4ms -> 86.4μs (19987% faster)


def test_sorter_large_reverse_sorted():
    # Large reverse-sorted list
    arr = list(range(999, -1, -1))
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 28.6ms -> 19.1ms (49.7% 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[:])  # 24.1ms -> 14.5ms (66.7% faster)


def test_sorter_large_strings():
    # Large list of random strings
    arr = ["".join(random.choices(string.ascii_letters, k=10)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 30.2ms -> 18.2ms (65.6% faster)


def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 24.8ms -> 15.8ms (57.2% faster)


def test_sorter_large_booleans():
    # Large list of boolean values
    arr = [random.choice([True, False]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])  # 24.6ms -> 11.8ms (108% faster)


# ------------------ MUTATION TESTING EDGE CASES ------------------


def test_sorter_mutation_no_side_effects():
    # Ensure that sorting a copy does not mutate the original list
    arr = [3, 2, 1]
    arr_copy = arr[:]
    sorter(arr_copy)  # 29.7μs -> 28.2μs (5.33% faster)


def test_sorter_returns_same_object():
    # Ensure the function returns the same list object (in-place sort)
    arr = [2, 1]
    codeflash_output = sorter(arr)
    result = codeflash_output  # 27.7μs -> 27.5μs (0.454% faster)


def test_sorter_idempotence():
    # Sorting an already sorted list should not change it
    arr = [1, 2, 3, 4]
    codeflash_output = sorter(arr[:])
    result1 = codeflash_output  # 28.5μs -> 28.0μs (1.64% faster)
    codeflash_output = sorter(result1[:])
    result2 = codeflash_output  # 26.9μs -> 25.0μs (7.49% faster)


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import copy  # used to ensure input lists are not mutated in-place
import random  # used for generating large random lists
import string  # used for string sorting tests

# imports
from code_to_optimize.bubble_sort import sorter

# unit tests

# --- Basic Test Cases ---


def test_sorter_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 28.8μs -> 30.2μs (4.42% slower)


def test_sorter_single_element():
    # Test sorting a list with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 31.4μs -> 30.2μs (4.14% faster)


def test_sorter_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 31.4μs -> 30.1μs (4.29% faster)


def test_sorter_reverse_sorted_list():
    # Test sorting a reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 32.9μs -> 30.5μs (8.07% faster)


def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    arr = [3, 1, 2, 3, 1]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 29.3μs -> 31.3μs (6.51% slower)


def test_sorter_negative_numbers():
    # Test sorting with negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 33.8μs -> 27.3μs (23.8% faster)


def test_sorter_floats():
    # Test sorting with floats
    arr = [1.1, 0.9, 3.3, 2.2]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 32.2μs -> 29.0μs (11.2% faster)


def test_sorter_mixed_int_float():
    # Test sorting with mixed ints and floats
    arr = [1, 2.2, 0, -1.1, 3]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 31.0μs -> 30.0μs (3.47% faster)


def test_sorter_strings():
    # Test sorting strings (lexicographical order)
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 28.9μs -> 27.9μs (3.58% faster)


def test_sorter_unicode_strings():
    # Test sorting unicode strings
    arr = ["ápple", "apple", "äpple"]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 29.5μs -> 29.3μs (0.569% faster)


# --- Edge Test Cases ---


def test_sorter_all_equal():
    # Test sorting a list where all elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 27.8μs -> 28.7μs (3.20% slower)


def test_sorter_large_negative_and_positive():
    # Test sorting a list with large negative and positive numbers
    arr = [999999, -999999, 0, 123456, -123456]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 33.7μs -> 28.9μs (16.6% faster)


def test_sorter_min_max_int():
    # Test sorting with Python's min/max int values (simulate 32-bit)
    arr = [2**31 - 1, -(2**31), 0]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 27.8μs -> 27.5μs (0.755% faster)


def test_sorter_min_max_float():
    # Test sorting with min/max float values
    arr = [float("-inf"), float("inf"), 0.0, -1.0, 1.0]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 29.6μs -> 29.0μs (2.16% faster)


def test_sorter_stability():
    # Test stability: equal elements retain original order
    class Item:
        def __init__(self, key, value):
            self.key = key
            self.value = value

        def __lt__(self, other):
            return self.key < other.key

        def __gt__(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")]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 30.9μs -> 33.9μs (8.86% slower)


def test_sorter_immutable_input():
    # Ensure the function does not mutate the input list (bubble sort is in-place, so this will fail)
    arr = [5, 3, 1]
    arr_copy = copy.deepcopy(arr)
    sorter(arr)  # 30.2μs -> 27.8μs (8.53% faster)
    # If we want to test for non-mutation, we'd need a different implementation


def test_sorter_with_nan():
    # Sorting with NaN should place NaN at the end (Python's default sort does this)
    arr = [1, float("nan"), 2]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 27.9μs -> 26.3μs (6.18% faster)


def test_sorter_strings_case_sensitive():
    # Test sorting strings with different cases (should be case-sensitive)
    arr = ["apple", "Banana", "banana", "Apple"]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 28.2μs -> 30.0μs (6.24% slower)


def test_sorter_mutable_elements():
    # Test sorting lists of lists (lexicographical order)
    arr = [[2], [1, 2], [1], [2, 1]]
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 29.2μs -> 26.7μs (9.20% faster)


# --- Large Scale Test Cases ---


def test_sorter_large_random_list():
    # Test sorting a large random list of integers
    arr = random.sample(range(-1000, 0), 1000)  # 1000 unique random ints
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 25.2ms -> 15.4ms (63.4% faster)


def test_sorter_large_duplicates():
    # Test sorting a 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  # 23.8ms -> 14.6ms (63.5% faster)


def test_sorter_large_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.copy())
    result = codeflash_output  # 29.8ms -> 17.8ms (67.6% faster)


def test_sorter_large_already_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 17.5ms -> 92.7μs (18798% 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.copy())
    result = codeflash_output  # 28.9ms -> 19.3ms (49.8% faster)


def test_sorter_large_all_equal():
    # Test sorting a large list where all elements are the same
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy())
    result = codeflash_output  # 17.8ms -> 83.0μs (21380% 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-mj0nhyxn and push.