⚡️ Speed up function sorter by 79%

[codeflash-ai]

📄 79% (0.79x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.07 seconds → 1.71 seconds (best of 6 runs)

📝 Explanation and details

The optimized code implements two key algorithmic improvements to the bubble sort that dramatically reduce execution time:

1. Early termination optimization: The code adds a swapped flag that tracks whether any swaps occurred in a pass. If no swaps happen, the array is already sorted and the algorithm can exit early. This is especially powerful for already-sorted or nearly-sorted inputs - as seen in the test results where sorted arrays show 17,000%+ speedups.

2. Reduced iteration bounds: The inner loop now runs len(arr) - 1 - i iterations instead of always len(arr) - 1. Since bubble sort moves the largest unsorted element to its final position in each pass, the last i elements are already in place and don't need to be checked again. This reduces the total comparisons from O(n²) to approximately half that in the worst case.

3. Tuple unpacking for swaps: 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 and concise.

The line profiler shows the optimized version performs ~55M operations vs ~114M in the original - nearly a 50% reduction in work. The 79% speedup is most pronounced on large datasets and already-sorted inputs, where early termination provides massive gains (up to 20,000% faster for sorted lists of 1000 elements). Even worst-case scenarios like reverse-sorted lists see 50-70% improvements due to the reduced iteration bounds.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 57 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	6.21ms	3.96ms	56.9%✅
test_bubble_sort.py::test_sort	756ms	514ms	47.1%✅
test_bubble_sort_conditional.py::test_sort	26.7μs	31.5μs	-15.3%⚠️
test_bubble_sort_import.py::test_sort	759ms	512ms	48.4%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	757ms	514ms	47.4%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	451ms	380μs	118364%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	239μs	171μs	39.8%✅

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string edge cases
import sys  # used for extreme integer values

# 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
    input_list = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 29.5μs -> 29.3μs (0.713% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    input_list = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 30.6μs -> 29.0μs (5.31% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    input_list = [3, 1, 2, 3, 2]
    expected = [1, 2, 2, 3, 3]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 30.2μs -> 29.4μs (2.70% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    input_list = [-1, -3, 2, 0, 1]
    expected = [-3, -1, 0, 1, 2]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 30.7μs -> 29.0μs (5.89% faster)

def test_sorter_basic_single_element():
    # Single element list
    input_list = [42]
    expected = [42]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 28.6μs -> 29.1μs (1.86% slower)

def test_sorter_basic_empty_list():
    # Empty list
    input_list = []
    expected = []
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 26.9μs -> 26.5μs (1.25% faster)

def test_sorter_basic_floats():
    # List with floats
    input_list = [3.1, 2.2, 5.5, 1.0]
    expected = [1.0, 2.2, 3.1, 5.5]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 32.3μs -> 29.8μs (8.24% faster)

def test_sorter_basic_mixed_int_float():
    # List with mixed ints and floats
    input_list = [1, 2.5, 0, 2]
    expected = [0, 1, 2, 2.5]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 28.4μs -> 31.3μs (9.32% slower)

# --- Edge Test Cases ---

def test_sorter_edge_all_equal():
    # All elements are equal
    input_list = [7, 7, 7, 7]
    expected = [7, 7, 7, 7]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 29.3μs -> 28.9μs (1.30% faster)

def test_sorter_edge_large_integers():
    # List with very large integers
    input_list = [sys.maxsize, -sys.maxsize-1, 0]
    expected = [-sys.maxsize-1, 0, sys.maxsize]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 30.4μs -> 27.0μs (12.7% faster)

def test_sorter_edge_small_integers():
    # List with very small integers
    input_list = [-999999999, 0, 999999999]
    expected = [-999999999, 0, 999999999]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 29.0μs -> 28.1μs (3.41% faster)

def test_sorter_edge_strings():
    # List of strings
    input_list = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 33.3μs -> 27.9μs (19.6% faster)

def test_sorter_edge_strings_with_empty():
    # List of strings with empty string
    input_list = ["", "a", "abc", "ab"]
    expected = ["", "a", "ab", "abc"]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 32.1μs -> 30.0μs (6.95% faster)

def test_sorter_edge_unicode_strings():
    # List of unicode strings
    input_list = ["éclair", "apple", "Éclair"]
    expected = ["Éclair", "apple", "éclair"]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 32.2μs -> 29.0μs (11.4% faster)

def test_sorter_edge_boolean():
    # List of booleans
    input_list = [True, False, True]
    expected = [False, True, True]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 29.5μs -> 28.7μs (2.76% faster)

def test_sorter_edge_none_in_list():
    # List containing None should raise TypeError
    input_list = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(input_list.copy()) # 37.6μs -> 38.3μs (1.85% slower)

def test_sorter_edge_mixed_types():
    # List with mixed types should raise TypeError
    input_list = [1, "a", 2.5]
    with pytest.raises(TypeError):
        sorter(input_list.copy()) # 37.0μs -> 36.0μs (2.66% faster)

def test_sorter_edge_list_of_lists():
    # List of lists should sort by their first elements
    input_list = [[3, 2], [1, 4], [2, 5]]
    expected = [[1, 4], [2, 5], [3, 2]]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 33.1μs -> 30.2μs (9.81% faster)

def test_sorter_edge_list_with_empty_lists():
    # List with empty lists
    input_list = [[], [1], [0]]
    expected = [[], [0], [1]]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 30.7μs -> 30.5μs (0.547% faster)

def test_sorter_edge_long_strings():
    # List with long strings
    input_list = ["a"*100, "b"*99, "a"*101]
    expected = ["a"*100, "a"*101, "b"*99]
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 33.0μs -> 31.7μs (3.94% faster)

def test_sorter_edge_custom_objects():
    # List with custom objects should raise TypeError
    class Dummy:
        pass
    input_list = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        sorter(input_list.copy()) # 36.5μs -> 36.5μs (0.230% slower)

# --- Large Scale Test Cases ---

def test_sorter_large_sorted():
    # Large sorted list
    input_list = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 16.3ms -> 93.3μs (17331% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    input_list = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 27.5ms -> 17.9ms (54.0% faster)

def test_sorter_large_random():
    # Large random list
    input_list = random.sample(range(1000), 1000)
    expected = sorted(input_list)
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 24.5ms -> 15.2ms (60.9% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    input_list = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(input_list)
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 22.0ms -> 12.9ms (70.1% faster)

def test_sorter_large_strings():
    # Large list of random strings
    input_list = [
        ''.join(random.choices(string.ascii_letters, k=10))
        for _ in range(1000)
    ]
    expected = sorted(input_list)
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 29.4ms -> 17.4ms (68.7% faster)

def test_sorter_large_floats():
    # Large list of random floats
    input_list = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(input_list)
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 23.7ms -> 14.7ms (61.7% faster)

def test_sorter_large_all_equal():
    # Large list where all elements are equal
    input_list = [42] * 1000
    expected = [42] * 1000
    codeflash_output = sorter(input_list.copy()); result = codeflash_output # 16.1ms -> 85.8μs (18688% 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 generating lists of 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
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.3μs -> 33.6μs (2.11% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.7μs -> 29.3μs (14.8% faster)

def test_sorter_basic_unsorted():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.7μs -> 29.7μs (3.22% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.3μs -> 30.3μs (3.30% slower)

def test_sorter_basic_negatives():
    # List with negative numbers
    arr = [-1, -3, 2, 0, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.2μs -> 30.8μs (1.63% slower)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 26.6μs -> 29.5μs (9.62% slower)

def test_sorter_basic_two_elements():
    # Two element list
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.5μs -> 29.5μs (6.91% slower)

def test_sorter_basic_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.0μs -> 30.0μs (3.33% faster)

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.2μs -> 28.9μs (2.59% slower)

def test_sorter_edge_large_negative():
    # List with large negative and positive numbers
    arr = [-1000000, 1000000, 0, -999999, 999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.0μs -> 29.3μs (2.27% faster)

def test_sorter_edge_mixed_types_int_float():
    # List with ints and floats
    arr = [3, 1.5, 2.2, 1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.8μs -> 32.2μs (1.55% faster)

def test_sorter_edge_strings():
    # List of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.3μs -> 32.3μs (3.10% faster)

def test_sorter_edge_mixed_case_strings():
    # List of strings with mixed cases
    arr = ["Banana", "apple", "Cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.7μs -> 28.8μs (6.51% faster)

def test_sorter_edge_unicode_strings():
    # List of unicode strings
    arr = ["ápple", "apple", "äpple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.0μs -> 31.0μs (0.135% slower)

def test_sorter_edge_floats():
    # List of floats
    arr = [3.2, 1.1, 4.4, 2.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.6μs -> 30.1μs (4.98% faster)

def test_sorter_edge_negative_floats():
    # List of negative floats
    arr = [-1.1, -3.3, -2.2, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.7μs -> 29.5μs (0.705% faster)

def test_sorter_edge_zeroes():
    # List with zeroes and other numbers
    arr = [0, 0, 1, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.9μs -> 27.7μs (15.2% faster)

def test_sorter_edge_large_identical():
    # Large list of identical elements
    arr = [5] * 100
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 143μs -> 34.3μs (318% faster)

def test_sorter_edge_min_max():
    # List with min and max integer values
    arr = [int(-1e9), int(1e9), 0, int(1e9)-1, int(-1e9)+1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.2μs -> 28.4μs (9.84% faster)

def test_sorter_edge_mutation():
    # Ensure function does not mutate input list (should return sorted, not mutate original)
    arr = [3, 2, 1]
    original = arr.copy()
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.8μs -> 30.0μs (0.694% slower)

def test_sorter_edge_boolean():
    # List of booleans (True > False)
    arr = [True, False, True, False]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.1μs -> 29.8μs (2.24% slower)

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

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-1000, 0), 999)  # 999 unique ints
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 23.8ms -> 14.6ms (62.6% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 16.2ms -> 87.5μs (18356% 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 # 27.2ms -> 18.0ms (51.0% 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 # 22.1ms -> 12.9ms (70.7% faster)

def test_sorter_large_strings():
    # Large list of random strings
    arr = [''.join(random.choices(string.ascii_lowercase, k=5)) for _ in range(500)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.01ms -> 3.99ms (75.5% faster)

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 23.7ms -> 14.7ms (61.6% faster)

def test_sorter_large_already_sorted_strings():
    # Large already sorted list of strings
    arr = [f"str_{i:04d}" for i in range(1000)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 22.2ms -> 105μs (20953% faster)

def test_sorter_large_reverse_sorted_strings():
    # Large reverse sorted list of strings
    arr = [f"str_{i:04d}" for i in range(999, -1, -1)]
    expected = [f"str_{i:04d}" for i in range(1000)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.3ms -> 20.7ms (60.9% 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-mf089z0l and push.