⚡️ Speed up function sorter by 78%

[codeflash-ai]

📄 78% (0.78x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 5.96 seconds → 3.36 seconds (best of 5 runs)

📝 Explanation and details

The optimized bubble sort implements two key algorithmic improvements that significantly reduce unnecessary operations:

1. Early termination with swap detection: Added a swapped flag that tracks whether any swaps occurred during a pass. If no swaps happen, the list is already sorted and the algorithm can exit early. This provides massive speedups for already-sorted or nearly-sorted data - the test results show 37,506% faster performance on large sorted lists.

2. Reduced comparisons per pass: Changed the inner loop from range(len(arr) - 1) to range(n - 1 - i). This avoids checking the last i elements in each pass since they're already in their final sorted positions. This reduces the total comparisons from O(n²) to approximately half that in practice.

3. Efficient swapping: Replaced the three-line temporary variable swap with Python's tuple unpacking (arr[j], arr[j + 1] = arr[j + 1], arr[j]), which is both more concise and slightly more efficient.

The line profiler shows the optimized version performs 14% fewer comparisons (44M vs 51M hits on the comparison line) and 20% fewer swap operations, leading to a 77% overall speedup.

These optimizations are particularly effective for:

• Already sorted data (enormous speedups: 36,000%+ faster)
• Nearly sorted data with few inversions
• Large datasets where the reduced comparison count compounds (65-72% faster on 1000-element random lists)
• Lists with many duplicate values (72% faster due to early termination)

Small lists show minimal improvement since the overhead is already low, but the algorithm maintains identical correctness across all test cases.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 62 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	11.3ms	7.34ms	53.4%✅
test_bubble_sort.py::test_sort	1.43s	985ms	45.4%✅
test_bubble_sort_conditional.py::test_sort	8.21μs	7.33μs	11.9%✅
test_bubble_sort_import.py::test_sort	1.43s	985ms	45.1%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	1.43s	980ms	46.2%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	896ms	428μs	208964%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	145μs	39.4μs	269%✅

🌀 Generated Regression Tests and Runtime

import random  # used for generating large scale random lists
import string  # used for edge cases with strings

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

# unit tests

# ---- BASIC TEST CASES ----

def test_sorter_basic_sorted():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 5.33μs (9.38% faster)

def test_sorter_basic_reverse_sorted():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.25μs -> 6.33μs (1.31% slower)

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted ascending
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.79μs -> 5.92μs (2.11% slower)

def test_sorter_basic_duplicates():
    # List with duplicates should handle and sort correctly
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.54μs -> 5.79μs (4.32% slower)

def test_sorter_basic_negative_numbers():
    # List with negative numbers should be sorted correctly
    arr = [-1, -3, 2, 0, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.88μs -> 6.00μs (2.08% slower)

def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 4.54μs (5.48% faster)

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 5.12μs (0.820% slower)

def test_sorter_basic_floats():
    # List of floats should be sorted correctly
    arr = [3.1, 2.2, 5.5, 4.4, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.79μs -> 7.83μs (0.523% slower)

def test_sorter_basic_mixed_int_float():
    # List with mixed ints and floats
    arr = [1, 3.5, 2, 2.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.75μs -> 6.58μs (2.54% faster)

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

def test_sorter_edge_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 4.38μs (7.61% slower)

def test_sorter_edge_all_identical():
    # All elements identical should remain unchanged
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.33μs -> 5.04μs (5.79% faster)

def test_sorter_edge_large_negative_and_positive():
    # Very large negative and positive numbers
    arr = [-1000000, 999999, 0, 123456, -654321]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.79μs -> 6.54μs (3.81% faster)

def test_sorter_edge_strings():
    # Sorting strings lexicographically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.71μs -> 5.88μs (2.84% slower)

def test_sorter_edge_single_char_strings():
    # Sorting single character strings
    arr = ["z", "a", "m", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.96μs -> 5.92μs (0.710% faster)

def test_sorter_edge_mixed_case_strings():
    # Sorting mixed case strings (capital letters come before lowercase)
    arr = ["Apple", "banana", "Banana", "apple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.88μs -> 5.75μs (2.17% faster)

def test_sorter_edge_boolean_values():
    # Sorting booleans: False < True
    arr = [True, False, True, False]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 5.79μs (3.61% faster)

def test_sorter_edge_mixed_types_raises():
    # Sorting mixed types (int and str) should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.46μs -> 3.67μs (5.70% slower)

def test_sorter_edge_none_in_list_raises():
    # Sorting list with None and ints should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.21μs -> 3.25μs (1.29% slower)

def test_sorter_edge_large_identical_elements():
    # Large list with all identical elements
    arr = [5] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.1ms -> 82.2μs (38912% faster)

def test_sorter_edge_sorted_descending():
    # Sorted descending, should sort ascending
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.5ms -> 34.1ms (50.9% faster)

# ---- 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.copy()); result = codeflash_output # 46.5ms -> 28.2ms (65.1% faster)

def test_sorter_large_random_floats():
    # Large list of random floats
    arr = [random.uniform(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 45.2ms -> 27.4ms (65.2% 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.copy()); result = codeflash_output # 50.9ms -> 30.1ms (68.9% faster)

def test_sorter_large_sorted():
    # Already sorted large list should remain unchanged
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.4ms -> 88.3μs (36573% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list should be sorted ascending
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.3ms -> 34.1ms (50.7% 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.copy()); result = codeflash_output # 41.6ms -> 24.2ms (71.6% 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.copy()); result = codeflash_output # 41.6ms -> 19.3ms (116% faster)

def test_sorter_large_edge_negative_positive():
    # Large list with negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 46.1ms -> 27.4ms (68.5% 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 test cases
import string  # used for non-integer 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_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 4.96μs -> 4.67μs (6.24% faster)

def test_single_element_list():
    # Test sorting a list with one element
    codeflash_output = sorter([42]) # 5.17μs -> 5.21μs (0.806% slower)

def test_sorted_list():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 5.92μs -> 5.25μs (12.7% faster)

def test_reverse_sorted_list():
    # Test sorting a reverse-sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 6.29μs -> 6.00μs (4.87% faster)

def test_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 5, 2]) # 6.08μs -> 6.00μs (1.38% faster)

def test_list_with_duplicates():
    # Test sorting a list with duplicate values
    codeflash_output = sorter([2, 3, 2, 1, 3, 1]) # 6.42μs -> 6.04μs (6.21% faster)

def test_list_with_negative_numbers():
    # Test sorting a list with negative values
    codeflash_output = sorter([-1, -3, 2, 0, -2]) # 6.38μs -> 5.79μs (10.1% faster)

def test_list_with_mixed_sign_numbers():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([0, -1, 1, -2, 2]) # 5.92μs -> 5.92μs (0.000% faster)

def test_list_with_floats():
    # Test sorting a list with float numbers
    codeflash_output = sorter([3.1, 2.2, 5.5, 1.0]) # 8.00μs -> 7.75μs (3.23% faster)

def test_list_with_integers_and_floats():
    # Test sorting a list with both integers and floats
    codeflash_output = sorter([3, 2.2, 5, 1.0]) # 6.92μs -> 6.58μs (5.06% faster)

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

def test_all_elements_equal():
    # Test sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7, 7]) # 5.62μs -> 5.12μs (9.76% faster)

def test_two_elements_sorted():
    # Test sorting a two-element list that is already sorted
    codeflash_output = sorter([1, 2]) # 4.96μs -> 4.75μs (4.38% faster)

def test_two_elements_unsorted():
    # Test sorting a two-element list that is not sorted
    codeflash_output = sorter([2, 1]) # 5.08μs -> 5.04μs (0.813% faster)

def test_large_range_of_values():
    # Test sorting a list with a large range of values
    arr = [1000, -1000, 500, 0, -500]
    codeflash_output = sorter(arr) # 6.96μs -> 6.79μs (2.44% faster)

def test_list_with_strings():
    # Test sorting a list of strings alphabetically
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr) # 6.21μs -> 5.96μs (4.20% faster)

def test_list_with_mixed_types():
    # Test sorting a list with mixed types (should raise TypeError)
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr) # 3.46μs -> 3.50μs (1.17% slower)

def test_list_with_none():
    # Test sorting a list containing None (should raise TypeError)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr) # 3.21μs -> 3.12μs (2.69% faster)

def test_list_with_booleans():
    # Test sorting a list with boolean values (False < True)
    arr = [True, False, True]
    # In Python, False==0, True==1, so sorting should be [False, True, True]
    codeflash_output = sorter(arr) # 5.58μs -> 5.54μs (0.740% faster)

def test_list_with_large_numbers():
    # Test sorting a list with very large numbers
    arr = [10**10, -10**10, 0, 10**5]
    codeflash_output = sorter(arr) # 6.25μs -> 6.08μs (2.75% faster)

def test_list_with_special_float_values():
    # Test sorting a list with inf and nan (nan should stay at end)
    arr = [float('inf'), float('-inf'), float('nan'), 0.0]
    # Sorting puts nan at the end, as comparisons with nan always fail
    codeflash_output = sorter(arr); result = codeflash_output # 6.42μs -> 6.29μs (2.00% faster)

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

def test_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 32.2ms -> 85.7μs (37506% faster)

def test_large_reverse_sorted_list():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()) # 51.4ms -> 34.1ms (50.8% faster)

def test_large_random_list():
    # Test sorting a large random list
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 47.2ms -> 28.5ms (65.8% faster)

def test_large_list_with_duplicates():
    # Test sorting a large list with many duplicate values
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 41.7ms -> 24.1ms (72.8% faster)

def test_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.copy()) # 51.0ms -> 29.9ms (70.4% faster)

def test_large_list_with_negative_and_positive():
    # Test sorting a large list with both negative and positive numbers
    arr = [random.randint(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 46.5ms -> 27.7ms (67.7% faster)

def test_large_list_with_floats():
    # Test sorting a large list with floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 45.4ms -> 27.1ms (67.6% faster)

# ------------------ MUTATION SENSITIVITY TESTS ------------------

@pytest.mark.parametrize("input_list, expected", [
    ([1, 2, 3], [1, 2, 3]),  # already sorted
    ([2, 1, 3], [1, 2, 3]),  # simple swap
    ([3, 2, 1], [1, 2, 3]),  # reverse order
    ([1, 1, 2], [1, 1, 2]),  # duplicates
    ([1, 2, 1], [1, 1, 2]),  # duplicates, unsorted
    ([0, -1, 1], [-1, 0, 1]),  # negatives
    ([0.1, 0.01, 0.001], [0.001, 0.01, 0.1]),  # floats
])
def test_mutation_sensitivity(input_list, expected):
    # These tests should fail if the sorting logic is mutated
    codeflash_output = sorter(input_list.copy()) # 40.6μs -> 40.4μs (0.518% 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-mgzlf0p1 and push.