⚡️ Speed up function sorter by 81%

[codeflash-ai]

📄 81% (0.81x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 5.83 seconds → 3.22 seconds (best of 5 runs)

📝 Explanation and details

The optimized code implements two key bubble sort optimizations that significantly improve performance:

1. Early Exit with Swap Detection: The algorithm tracks whether any swaps occurred during a pass using a swapped flag. If no swaps happen, the array is fully sorted and the algorithm exits early. This provides massive speedups for already-sorted or nearly-sorted data (35,765% faster for sorted arrays, 36,109% faster for arrays with all duplicates).

2. Reduced Comparison Range: Each pass reduces the inner loop range by i elements (range(n - i - 1) vs range(len(arr) - 1)), since the last i elements are guaranteed to be in their final sorted positions after i passes. This eliminates unnecessary comparisons.

3. Tuple Unpacking for Swaps: Replaces 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 eliminates one variable assignment per swap.

The line profiler shows the optimized version performs ~53M inner loop iterations vs ~110M in the original (52% reduction), directly correlating to the 81% runtime improvement. The optimizations are particularly effective for:

• Already sorted data (early exit after first pass)
• Data with many duplicates (early exit when no more swaps needed)
• Large datasets (reduced comparisons compound the savings)

Even for worst-case scenarios like reverse-sorted arrays, the reduced comparison range still provides 54-74% speedups.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 30 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	12.1ms	7.64ms	58.1%✅
test_bubble_sort.py::test_sort	1.49s	1.01s	46.7%✅
test_bubble_sort_conditional.py::test_sort	14.1μs	13.8μs	2.42%✅
test_bubble_sort_import.py::test_sort	1.49s	1.01s	47.2%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	1.49s	1.01s	47.6%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	921ms	453μs	203021%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	193μs	80.1μs	142%✅

🌀 Generated Regression Tests and Runtime

import random  # used for large scale random test generation
import string  # used for string sorting tests

# 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]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 14.2μs -> 12.5μs (13.7% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 13.8μs -> 13.2μs (4.72% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 13.5μs -> 12.7μs (6.24% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [3, 1, 2, 3, 2]
    expected = [1, 2, 2, 3, 3]
    codeflash_output = sorter(arr.copy()) # 13.2μs -> 12.5μs (5.65% 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.copy()) # 13.3μs -> 12.5μs (6.67% faster)

def test_sorter_basic_floats():
    # List with floats
    arr = [3.1, 2.4, 5.6, 1.2]
    expected = [1.2, 2.4, 3.1, 5.6]
    codeflash_output = sorter(arr.copy()) # 14.9μs -> 14.7μs (1.14% faster)

def test_sorter_basic_mixed_int_float():
    # List with mixed ints and floats
    arr = [2, 1.5, 3, 0.5]
    expected = [0.5, 1.5, 2, 3]
    codeflash_output = sorter(arr.copy()) # 14.5μs -> 14.2μs (2.65% faster)

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(arr.copy()) # 13.1μs -> 13.1μs (0.000% faster)

def test_sorter_basic_single_element():
    # List with one element
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()) # 12.0μs -> 11.9μs (0.699% faster)

def test_sorter_basic_empty():
    # Empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()) # 11.7μs -> 11.8μs (0.715% slower)

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

def test_sorter_edge_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7]
    expected = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()) # 12.6μs -> 12.2μs (3.76% faster)

def test_sorter_edge_large_negative():
    # Large negative numbers
    arr = [-99999, -1, -50000, -100, -99998]
    expected = [-99999, -99998, -50000, -100, -1]
    codeflash_output = sorter(arr.copy()) # 14.2μs -> 13.6μs (3.97% faster)

def test_sorter_edge_large_positive():
    # Large positive numbers
    arr = [99999, 1, 50000, 100, 99998]
    expected = [1, 100, 50000, 99998, 99999]
    codeflash_output = sorter(arr.copy()) # 14.1μs -> 13.7μs (3.04% faster)

def test_sorter_edge_mixed_types():
    # Mixed types (should raise TypeError)
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 55.5μs -> 55.2μs (0.604% faster)

def test_sorter_edge_none_values():
    # List with None values (should raise TypeError)
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 54.9μs -> 54.7μs (0.380% faster)

def test_sorter_edge_large_float_precision():
    # Floats with high precision
    arr = [1.0000001, 1.0000002, 1.0000000]
    expected = [1.0000000, 1.0000001, 1.0000002]
    codeflash_output = sorter(arr.copy()) # 14.1μs -> 13.4μs (4.96% faster)

def test_sorter_edge_unicode_strings():
    # Unicode strings
    arr = ["ápple", "apple", "banana"]
    expected = ["apple", "banana", "ápple"]
    codeflash_output = sorter(arr.copy()) # 13.8μs -> 13.7μs (0.905% faster)

def test_sorter_edge_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    expected = ["", "", "a", "b"]
    codeflash_output = sorter(arr.copy()) # 13.3μs -> 13.1μs (1.27% faster)

def test_sorter_edge_boolean_values():
    # List with booleans (should sort as 0/1)
    arr = [True, False, True]
    expected = [False, True, True]
    codeflash_output = sorter(arr.copy()) # 12.7μs -> 12.7μs (0.000% faster)


def test_sorter_edge_large_min_max():
    # List with min/max integer values
    arr = [-(2**31), 0, 2**31-1]
    expected = [-(2**31), 0, 2**31-1]
    codeflash_output = sorter(arr.copy()) # 13.1μs -> 12.6μs (3.96% faster)

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

def test_sorter_large_random_integers():
    # Large random list of integers
    arr = random.sample(range(-1000, 0), 1000)  # 1000 unique integers
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 48.5ms -> 28.6ms (69.5% faster)

def test_sorter_large_many_duplicates():
    # Large list with many duplicates
    arr = [42] * 1000
    expected = [42] * 1000
    codeflash_output = sorter(arr.copy()) # 32.8ms -> 90.6μs (36109% 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.copy()) # 54.7ms -> 35.4ms (54.6% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 33.2ms -> 92.6μs (35765% faster)

def test_sorter_large_strings():
    # Large list of random strings
    arr = [''.join(random.choices(string.ascii_lowercase, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 53.5ms -> 31.5ms (70.1% 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()) # 46.9ms -> 27.9ms (68.4% faster)

def test_sorter_large_alternating_signs():
    # Large list alternating positive and negative
    arr = [i if i % 2 == 0 else -i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 44.1ms -> 25.3ms (74.2% faster)

def test_sorter_large_boolean_values():
    # Large list of booleans
    arr = [True, False] * 500  # 1000 elements
    expected = [False] * 500 + [True] * 500
    codeflash_output = sorter(arr.copy()) # 41.3ms -> 18.5ms (123% faster)

def test_sorter_large_empty_strings():
    # Large list of empty strings
    arr = [""] * 1000
    expected = [""] * 1000
    codeflash_output = sorter(arr.copy()) # 35.7ms -> 79.5μs (44800% faster)

def test_sorter_large_already_sorted_floats():
    # Large already sorted floats
    arr = [i * 0.1 for i in range(1000)]
    expected = [i * 0.1 for i in range(1000)]
    codeflash_output = sorter(arr.copy()) # 32.9ms -> 259μs (12594% 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-mgzma75j and push.