⚡️ Speed up function sorter by 80%

[codeflash-ai]

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

⏱️ Runtime : 5.73 seconds → 3.19 seconds (best of 5 runs)

📝 Explanation and details

The optimized code achieves a 79% speedup through three key algorithmic improvements to the bubble sort:

1. Reduced Inner Loop Range: Instead of always iterating range(len(arr) - 1), the optimized version uses range(n - i - 1). This leverages bubble sort's property that after each pass, the largest elements settle at the end, so we don't need to check already-sorted positions.

2. Early Exit Optimization: The swapped flag tracks whether any swaps occurred during a pass. If no swaps happen, the array is already sorted and we can exit early via break. This is especially powerful for already-sorted or nearly-sorted data.

3. Direct Tuple Swap: Replaces the traditional 3-line swap using a temporary variable with Python's tuple unpacking: arr[j], arr[j + 1] = arr[j + 1], arr[j]. This eliminates one variable assignment per swap.

Performance Impact by Test Case:

• Already sorted data: Massive gains (37,680% faster for 1000 elements) due to early exit after first pass
• Nearly sorted data: Exceptional performance (27,580% faster) as only a few passes are needed
• All equal elements: Huge improvement (38,346% faster) with immediate early exit
• Random/reverse sorted data: Moderate gains (50-70% faster) from reduced inner loop iterations
• Small arrays: Minimal improvement (1-11%) as overhead dominates

The line profiler shows the optimized version performs significantly fewer iterations in the inner loop (43.6M vs 49.8M hits), demonstrating the effectiveness of the reduced range optimization.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 59 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	11.2ms	7.23ms	54.7%✅
test_bubble_sort.py::test_sort	1.42s	971ms	46.0%✅
test_bubble_sort_conditional.py::test_sort	8.08μs	7.58μs	6.59%✅
test_bubble_sort_import.py::test_sort	1.42s	969ms	46.5%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	1.42s	974ms	45.6%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	886ms	424μs	208785%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	144μs	36.9μs	291%✅

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
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 should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.71μs -> 6.25μs (7.33% 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 # 6.62μs -> 6.54μs (1.28% faster)

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 # 6.04μs -> 5.96μs (1.39% faster)

def test_sorter_basic_duplicates():
    # List with duplicates should sort and keep duplicates
    arr = [2, 3, 2, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.92μs -> 5.71μs (3.64% faster)

def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 4.83μs (4.30% slower)

def test_sorter_basic_two_elements():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 5.04μs (0.000% faster)

def test_sorter_basic_all_equal():
    # All elements equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.33μs -> 4.96μs (7.54% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [3, -1, 2, -5, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.29μs -> 5.88μs (7.10% faster)

def test_sorter_basic_mixed_signs():
    # List with both positive and negative numbers
    arr = [-2, 4, 0, -1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 5.71μs (2.17% faster)

def test_sorter_basic_floats():
    # List with floats
    arr = [2.2, 1.1, 3.3, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.38μs -> 7.33μs (0.573% faster)

def test_sorter_basic_mixed_int_float():
    # List with both ints and floats
    arr = [3, 1.5, 2, 0.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.33μs -> 6.79μs (7.98% faster)

def test_sorter_basic_strings():
    # List of strings should sort lexicographically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.92μs -> 5.58μs (5.98% faster)

def test_sorter_basic_empty():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.17μs -> 4.33μs (3.83% slower)

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

def test_sorter_edge_large_negative_numbers():
    # List with very large negative numbers
    arr = [-10**10, -10**5, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.79μs -> 5.42μs (6.92% faster)

def test_sorter_edge_large_positive_numbers():
    # List with very large positive numbers
    arr = [10**10, 10**5, 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.46μs -> 6.17μs (4.75% faster)

def test_sorter_edge_mixed_types():
    # List with mixed types should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.29μs -> 3.33μs (1.23% slower)

def test_sorter_edge_none_in_list():
    # List with None should raise TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.08μs -> 3.12μs (1.34% slower)

def test_sorter_edge_inf_nan():
    # List with inf and nan floats
    arr = [float('inf'), float('-inf'), float('nan'), 0]
    # nan is always placed at the end in sorted()
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.50μs -> 5.88μs (10.6% faster)
    # sorted() puts nan last, so we mimic that
    expected = [float('-inf'), 0, float('inf'), float('nan')]

def test_sorter_edge_strings_with_special_chars():
    # List of strings with special characters
    arr = ["!start", "middle", "#hash", "end"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.33μs -> 6.04μs (4.82% faster)

def test_sorter_edge_unicode_strings():
    # List of unicode strings
    arr = ["á", "a", "ä", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.58μs -> 6.38μs (3.26% faster)

def test_sorter_edge_empty_string_elements():
    # List with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 5.58μs (4.46% faster)


def test_sorter_edge_boolean_elements():
    # List with booleans (bool is subclass of int)
    arr = [True, False, 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.92μs -> 6.67μs (3.75% faster)

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

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-1000, 0), 1000)  # 1000 unique ints
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 46.2ms -> 27.5ms (67.6% 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 # 41.2ms -> 23.8ms (72.8% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.9ms -> 84.5μs (37680% 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 # 50.9ms -> 33.8ms (50.6% 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 # 50.6ms -> 29.6ms (70.8% 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 # 44.8ms -> 26.7ms (67.7% faster)

def test_sorter_large_all_equal():
    # Large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.6ms -> 82.2μs (38346% faster)

def test_sorter_large_alternating():
    # Large list alternating between two values
    arr = [1, 2] * 500
    expected = [1] * 500 + [2] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 36.8ms -> 16.6ms (121% faster)

def test_sorter_large_nearly_sorted():
    # Large list that is nearly sorted except for a few elements
    arr = list(range(1000))
    arr[500], arr[501] = arr[501], arr[500]  # swap two elements
    arr[998], arr[999] = arr[999], arr[998]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.0ms -> 115μs (27580% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
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():
    # Edge case: empty input
    codeflash_output = sorter([]) # 5.17μs -> 5.38μs (3.87% slower)

def test_sorter_single_element():
    # Edge case: single element input
    codeflash_output = sorter([42]) # 5.33μs -> 5.25μs (1.58% faster)

def test_sorter_two_elements_sorted():
    # Already sorted
    codeflash_output = sorter([1, 2]) # 5.25μs -> 5.17μs (1.63% faster)

def test_sorter_two_elements_unsorted():
    # Needs sorting
    codeflash_output = sorter([2, 1]) # 5.12μs -> 5.04μs (1.67% faster)

def test_sorter_multiple_elements_sorted():
    # Already sorted
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 5.54μs -> 5.12μs (8.14% faster)

def test_sorter_multiple_elements_reverse():
    # Reverse sorted
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 6.25μs -> 5.62μs (11.1% faster)

def test_sorter_multiple_elements_random():
    # Random order
    codeflash_output = sorter([3, 1, 4, 5, 2]) # 5.96μs -> 5.67μs (5.13% faster)

def test_sorter_with_duplicates():
    # List with duplicate values
    codeflash_output = sorter([3, 1, 2, 3, 2]) # 5.75μs -> 5.42μs (6.15% faster)

def test_sorter_with_negative_numbers():
    # List with negative numbers
    codeflash_output = sorter([0, -1, -3, 2, 1]) # 5.96μs -> 5.75μs (3.62% faster)

def test_sorter_with_mixed_signs():
    # List with both positive and negative numbers
    codeflash_output = sorter([-10, 5, 0, -2, 3]) # 6.12μs -> 5.83μs (5.01% faster)

def test_sorter_with_all_equal():
    # All elements are the same
    codeflash_output = sorter([7, 7, 7, 7]) # 5.00μs -> 4.79μs (4.36% faster)

# --- Edge Test Cases ---

def test_sorter_large_numbers():
    # Very large and very small integers
    arr = [999999999, -999999999, 0, 123456789, -123456789]
    expected = [-999999999, -123456789, 0, 123456789, 999999999]
    codeflash_output = sorter(arr) # 7.12μs -> 6.75μs (5.56% faster)

def test_sorter_min_max_int():
    # Python int can be arbitrarily large, but test with typical 32-bit boundaries
    arr = [2**31-1, -2**31, 0]
    expected = [-2**31, 0, 2**31-1]
    codeflash_output = sorter(arr) # 5.88μs -> 5.46μs (7.62% faster)

def test_sorter_already_sorted_with_duplicates():
    # Already sorted, but with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    expected = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr) # 6.38μs -> 5.12μs (24.4% faster)

def test_sorter_reverse_sorted_with_duplicates():
    # Reverse sorted, with duplicates
    arr = [5, 4, 4, 3, 2, 2, 1]
    expected = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr) # 7.08μs -> 6.71μs (5.57% faster)

def test_sorter_non_integer_values():
    # Should fail if non-integer values are present
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr) # 3.50μs -> 3.42μs (2.46% faster)

def test_sorter_floats():
    # If floats are allowed, should sort correctly
    arr = [1.5, 2.3, -0.7, 0.0]
    expected = [-0.7, 0.0, 1.5, 2.3]
    codeflash_output = sorter(arr) # 7.33μs -> 7.21μs (1.73% faster)

def test_sorter_mixed_int_float():
    # Mixed ints and floats
    arr = [1, 2.2, 0, -1.1]
    expected = [-1.1, 0, 1, 2.2]
    codeflash_output = sorter(arr) # 7.21μs -> 6.88μs (4.84% faster)

def test_sorter_mutation_of_input():
    # Ensure the input list is mutated (bubble sort sorts in-place)
    arr = [3, 2, 1]
    sorter(arr) # 5.29μs -> 5.42μs (2.29% slower)

def test_sorter_large_negative_values():
    # Large negative values
    arr = [-999999999, -888888888, -777777777]
    expected = [-999999999, -888888888, -777777777]
    codeflash_output = sorter(arr) # 5.42μs -> 5.21μs (3.99% faster)

# --- Large Scale Test Cases ---

def test_sorter_large_list_sorted():
    # Large sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 32.0ms -> 83.8μs (38066% faster)

def test_sorter_large_list_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 50.8ms -> 33.6ms (51.5% faster)

def test_sorter_large_list_random():
    # Large random list
    import random
    arr = list(range(1000))
    random.shuffle(arr)
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 44.8ms -> 27.5ms (62.9% faster)

def test_sorter_large_list_duplicates():
    # Large list with many duplicates
    arr = [5]*500 + [3]*250 + [7]*250
    expected = [3]*250 + [5]*500 + [7]*250
    codeflash_output = sorter(arr) # 36.4ms -> 16.4ms (122% faster)

def test_sorter_large_list_negative_and_positive():
    # Large list with negative and positive numbers
    arr = list(range(-500, 500))
    expected = list(range(-500, 500))
    random_arr = arr[:]
    import random
    random.shuffle(random_arr)
    codeflash_output = sorter(random_arr) # 45.0ms -> 27.8ms (62.2% faster)

# --- Determinism Test Case ---

def test_sorter_determinism():
    # The same input should always yield the same output
    arr = [4, 2, 5, 1, 3]
    codeflash_output = sorter(arr[:]); result1 = codeflash_output # 6.92μs -> 6.83μs (1.21% faster)
    codeflash_output = sorter(arr[:]); result2 = codeflash_output # 5.42μs -> 5.08μs (6.55% faster)

# --- Sorting Stability Test Case ---

def test_sorter_stability():
    # Bubble sort is stable, so equal elements should retain their original order
    # We'll use tuples (value, index) to check stability
    arr = [(2, 'a'), (1, 'b'), (2, 'c'), (1, 'd')]
    # Sort by first element
    expected = [(1, 'b'), (1, 'd'), (2, 'a'), (2, 'c')]
    # Custom comparator for tuples
    def tuple_sorter(arr):
        for i in range(len(arr)):
            for j in range(len(arr) - 1):
                if arr[j][0] > arr[j + 1][0]:
                    temp = arr[j]
                    arr[j] = arr[j + 1]
                    arr[j + 1] = temp
        return arr
# 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-mh1408v8 and push.