⚡️ Speed up function `sorter` by 82% #793

codeflash-ai · 2025-10-03T19:04:40Z

📄 82% (0.82x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

⏱️ Runtime : 3.17 seconds → 1.74 seconds (best of 6 runs)

📝 Explanation and details

The optimized code implements three key improvements to the bubble sort algorithm:

1. Reduced comparisons per pass: Changed range(len(arr) - 1) to range(n - 1 - i). This eliminates redundant comparisons since bubble sort guarantees the largest i elements are already in their final positions after i passes. This reduces the inner loop iterations from ~116M to ~56M hits.

2. Early termination with swap detection: Added a swapped flag that tracks whether any swaps occurred during a pass. If no swaps happen, the array is already sorted and the algorithm can exit early. This is particularly effective for already-sorted or nearly-sorted data, where the algorithm can terminate in O(n) time instead of O(n²).

3. Optimized swap operation: Replaced the three-line temporary variable swap with Python's tuple unpacking (arr[j], arr[j + 1] = arr[j + 1], arr[j]). This is more efficient as it's implemented at the bytecode level.

Performance gains by test case type:

Already sorted lists: Massive improvements (17,000%+ faster) due to early termination after one pass
Lists with identical elements: Significant speedup (18,000%+ faster) as no swaps are needed
Random/unsorted lists: Moderate improvements (50-75% faster) from reduced comparisons
Small lists: Minor improvements (5-15% faster) as optimizations have less impact

The optimized version maintains the same O(n²) worst-case complexity but achieves O(n) best-case performance for sorted inputs, explaining the dramatic speedup in many test scenarios.

✅ Correctness verification report:

Test	Status
⏪ Replay Tests	🔘 None Found
⚙️ Existing Unit Tests	✅ 21 Passed
🔎 Concolic Coverage Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 62 Passed
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
`benchmarks/test_benchmark_bubble_sort.py::test_sort2`	6.25ms	3.94ms	58.8%✅
`test_bubble_sort.py::test_sort`	768ms	516ms	48.7%✅
`test_bubble_sort_conditional.py::test_sort`	32.1μs	28.3μs	13.4%✅
`test_bubble_sort_import.py::test_sort`	778ms	512ms	52.0%✅
`test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class`	786ms	513ms	53.1%✅
`test_bubble_sort_parametrized.py::test_sort_parametrized`	452ms	382μs	117966%✅
`test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized`	249μs	177μs	40.6%✅

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for testing string sorting

# 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():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 30.8μs -> 28.2μs (9.46% faster)

def test_sorter_single_element():
    # Test sorting a list with one element
    codeflash_output = sorter([42]) # 30.0μs -> 28.2μs (6.19% faster)

def test_sorter_sorted_list():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 29.8μs -> 27.9μs (7.02% faster)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 31.5μs -> 29.2μs (7.84% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 2, 5]) # 32.0μs -> 29.4μs (8.64% faster)

def test_sorter_list_with_duplicates():
    # Test sorting a list with duplicate values
    codeflash_output = sorter([2, 3, 2, 1, 4, 1]) # 32.1μs -> 27.6μs (16.3% faster)

def test_sorter_list_with_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -2, 0, 2, 1]) # 31.2μs -> 27.2μs (14.7% faster)

def test_sorter_list_with_mixed_sign_numbers():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([0, -1, 5, -10, 3]) # 31.0μs -> 29.8μs (4.20% faster)

def test_sorter_list_with_floats():
    # Test sorting a list with floating point numbers
    codeflash_output = sorter([3.1, 2.4, 5.6, 1.1]) # 32.9μs -> 30.8μs (6.77% faster)

def test_sorter_list_with_integers_and_floats():
    # Test sorting a list with both integers and floats
    codeflash_output = sorter([1, 2.2, 0.5, 3]) # 31.5μs -> 31.0μs (1.61% faster)

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

def test_sorter_all_identical_elements():
    # Test sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7]) # 32.0μs -> 25.5μs (25.1% faster)

def test_sorter_two_elements_sorted():
    # Test sorting a list with two sorted elements
    codeflash_output = sorter([1, 2]) # 29.7μs -> 28.6μs (3.79% faster)

def test_sorter_two_elements_unsorted():
    # Test sorting a list with two unsorted elements
    codeflash_output = sorter([2, 1]) # 30.0μs -> 28.5μs (5.27% faster)

def test_sorter_large_negative_numbers():
    # Test sorting a list with large negative numbers
    codeflash_output = sorter([-1000000, -999999, -1000001]) # 30.2μs -> 29.0μs (4.16% faster)

def test_sorter_large_positive_numbers():
    # Test sorting a list with large positive numbers
    codeflash_output = sorter([1000000, 999999, 1000001]) # 30.0μs -> 29.5μs (1.70% faster)

def test_sorter_list_with_zeroes():
    # Test sorting a list with zeroes and other numbers
    codeflash_output = sorter([0, 0, 1, -1]) # 30.0μs -> 28.0μs (6.98% faster)

def test_sorter_list_with_min_max_values():
    # Test sorting a list with min and max integer values
    min_int = -2**31
    max_int = 2**31 - 1
    codeflash_output = sorter([max_int, min_int, 0]) # 29.1μs -> 29.2μs (0.427% slower)

def test_sorter_list_with_strings():
    # Test sorting a list of strings alphabetically
    codeflash_output = sorter(['banana', 'apple', 'cherry']) # 30.1μs -> 28.1μs (7.27% faster)

def test_sorter_list_with_mixed_case_strings():
    # Test sorting a list of strings with mixed cases
    codeflash_output = sorter(['Banana', 'apple', 'Cherry']) # 30.2μs -> 27.3μs (10.7% faster)

def test_sorter_list_with_empty_strings():
    # Test sorting a list with empty strings
    codeflash_output = sorter(['', 'a', 'b']) # 27.9μs -> 26.7μs (4.53% faster)

def test_sorter_list_with_single_character_strings():
    # Test sorting a list with single-character strings
    codeflash_output = sorter(['d', 'a', 'c', 'b']) # 31.9μs -> 28.0μs (13.8% faster)

def test_sorter_list_with_boolean_values():
    # Test sorting a list with boolean values (False < True)
    codeflash_output = sorter([True, False, True, False]) # 30.4μs -> 27.8μs (9.61% faster)

def test_sorter_list_with_none_values():
    # Test sorting a list with None values should raise TypeError
    with pytest.raises(TypeError):
        sorter([None, 1, 2]) # 36.7μs -> 37.9μs (3.19% slower)

def test_sorter_list_with_incomparable_types():
    # Test sorting a list with incomparable types should raise TypeError
    with pytest.raises(TypeError):
        sorter([1, 'a', 2]) # 37.0μs -> 35.8μs (3.38% faster)



def test_sorter_list_with_dict_elements():
    # Test sorting a list with dict elements should raise TypeError
    with pytest.raises(TypeError):
        sorter([{'a': 1}, {'b': 2}]) # 41.4μs -> 38.6μs (7.12% faster)

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

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list
    large_list = list(range(1000))
    codeflash_output = sorter(large_list.copy()) # 16.2ms -> 92.0μs (17526% faster)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    large_list = list(range(999, -1, -1))
    codeflash_output = sorter(large_list.copy()) # 28.1ms -> 18.2ms (54.4% faster)

def test_sorter_large_random_list():
    # Test sorting a large random list of integers
    random.seed(42)  # deterministic
    large_list = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(large_list)
    codeflash_output = sorter(large_list.copy()) # 24.2ms -> 14.5ms (66.8% faster)

def test_sorter_large_list_with_duplicates():
    # Test sorting a large list with many duplicate values
    large_list = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(large_list)
    codeflash_output = sorter(large_list.copy()) # 22.5ms -> 13.1ms (71.0% faster)

def test_sorter_large_list_of_strings():
    # Test sorting a large list of random strings
    random.seed(123)
    large_list = [
        ''.join(random.choices(string.ascii_lowercase, k=5))
        for _ in range(1000)
    ]
    expected = sorted(large_list)
    codeflash_output = sorter(large_list.copy()) # 30.2ms -> 17.3ms (74.2% faster)

def test_sorter_large_list_of_floats():
    # Test sorting a large list of random floats
    random.seed(456)
    large_list = [random.uniform(-1000.0, 1000.0) for _ in range(1000)]
    expected = sorted(large_list)
    codeflash_output = sorter(large_list.copy()) # 24.7ms -> 15.1ms (64.1% faster)

def test_sorter_large_list_all_identical():
    # Test sorting a large list where all elements are identical
    large_list = [42] * 1000
    codeflash_output = sorter(large_list.copy()) # 16.2ms -> 85.0μs (18956% faster)

def test_sorter_large_list_with_extreme_values():
    # Test sorting a large list with extreme values included
    large_list = [random.randint(-10000, 10000) for _ in range(998)] + [-2**31, 2**31-1]
    expected = sorted(large_list)
    codeflash_output = sorter(large_list.copy()) # 24.2ms -> 14.7ms (64.6% 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 string test 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_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 38.5μs -> 32.0μs (20.2% faster)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [5, 3, 1, 4, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.8μs -> 35.4μs (1.53% slower)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [4, 2, 2, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.5μs -> 31.5μs (3.17% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.9μs -> 34.5μs (7.49% slower)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.9μs -> 29.3μs (12.2% faster)

def test_sorter_basic_two_elements():
    # Two element list
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.0μs -> 31.4μs (1.86% faster)

def test_sorter_basic_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.5μs -> 28.5μs (6.87% faster)

# --- Edge Test Cases ---

def test_sorter_edge_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.3μs -> 29.9μs (8.09% faster)

def test_sorter_edge_reverse_sorted():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.3μs -> 30.9μs (4.72% faster)

def test_sorter_edge_mixed_types():
    # List with mixed types (should raise TypeError)
    arr = [1, 'a', 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 45.8μs -> 37.8μs (20.9% faster)

def test_sorter_edge_strings():
    # List of strings
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.6μs -> 30.8μs (5.96% faster)

def test_sorter_edge_floats():
    # List of floats
    arr = [2.5, 1.1, 3.3, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 35.5μs -> 31.8μs (11.7% faster)

def test_sorter_edge_large_negative_and_positive():
    # List with large negative and positive numbers
    arr = [-1000000, 1000000, 0, 999999, -999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.5μs -> 31.1μs (10.9% faster)

def test_sorter_edge_boolean_values():
    # List with boolean values (should sort False < True)
    arr = [True, False, True, False]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.8μs -> 30.5μs (1.09% faster)

def test_sorter_edge_none_values():
    # List with None values (should raise TypeError)
    arr = [1, None, 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 37.4μs -> 36.4μs (2.75% faster)

def test_sorter_edge_mutation():
    # Ensure input list is mutated in-place
    arr = [3, 2, 1]
    sorter(arr) # 30.8μs -> 29.1μs (5.73% faster)

# --- Large Scale Test Cases ---

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 16.2ms -> 93.0μs (17341% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.9ms -> 18.0ms (54.8% faster)

def test_sorter_large_random():
    # Large random list
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.7ms -> 15.3ms (61.9% 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 # 22.5ms -> 13.0ms (72.9% 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()); result = codeflash_output # 30.6ms -> 17.9ms (71.5% 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 # 16.2ms -> 87.5μs (18445% faster)

# --- Additional Edge Cases ---

def test_sorter_edge_min_max_int():
    # List with min and max integer values
    arr = [-(2**31), 0, 2**31-1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.1μs -> 29.9μs (3.90% faster)

def test_sorter_edge_min_max_float():
    # List with min and max float values
    arr = [float('-inf'), 0.0, float('inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.2μs -> 29.5μs (2.55% faster)


def test_sorter_edge_large_negative():
    # Large list with only negative numbers
    arr = [random.randint(-10000, -1) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.7ms -> 14.7ms (67.7% faster)

def test_sorter_edge_large_floats():
    # Large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.7ms -> 14.9ms (66.0% faster)

# --- Determinism Test ---

def test_sorter_determinism():
    # Ensure repeated calls give same result
    arr = [5, 3, 1, 4, 2]
    codeflash_output = sorter(arr.copy()); result1 = codeflash_output # 35.8μs -> 31.1μs (15.1% faster)
    codeflash_output = sorter(arr.copy()); result2 = codeflash_output # 29.0μs -> 26.7μs (8.75% faster)

# --- Stability Test ---

def test_sorter_stability():
    # Test stability: items with same value should keep relative order
    class Item:
        def __init__(self, value, label):
            self.value = value
            self.label = label
        def __lt__(self, other):
            return self.value < other.value
        def __eq__(self, other):
            return self.value == other.value and self.label == other.label
        def __repr__(self):
            return f"Item({self.value}, {self.label})"
    items = [Item(1, 'a'), Item(1, 'b'), Item(2, 'c'), Item(2, 'd')]
    # After sorting, 'a' should come before 'b', 'c' before 'd'
    codeflash_output = sorter(items.copy()); result = codeflash_output # 35.0μs -> 34.4μs (1.94% 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-mgb7r1vd and push.

The optimized code implements three key improvements to the bubble sort algorithm: **1. Reduced comparisons per pass**: Changed `range(len(arr) - 1)` to `range(n - 1 - i)`. This eliminates redundant comparisons since bubble sort guarantees the largest `i` elements are already in their final positions after `i` passes. This reduces the inner loop iterations from ~116M to ~56M hits. **2. Early termination with swap detection**: Added a `swapped` flag that tracks whether any swaps occurred during a pass. If no swaps happen, the array is already sorted and the algorithm can exit early. This is particularly effective for already-sorted or nearly-sorted data, where the algorithm can terminate in O(n) time instead of O(n²). **3. Optimized swap operation**: Replaced the three-line temporary variable swap with Python's tuple unpacking (`arr[j], arr[j + 1] = arr[j + 1], arr[j]`). This is more efficient as it's implemented at the bytecode level. **Performance gains by test case type**: - **Already sorted lists**: Massive improvements (17,000%+ faster) due to early termination after one pass - **Lists with identical elements**: Significant speedup (18,000%+ faster) as no swaps are needed - **Random/unsorted lists**: Moderate improvements (50-75% faster) from reduced comparisons - **Small lists**: Minor improvements (5-15% faster) as optimizations have less impact The optimized version maintains the same O(n²) worst-case complexity but achieves O(n) best-case performance for sorted inputs, explaining the dramatic speedup in many test scenarios.

codeflash-ai bot requested a review from HeshamHM28 October 3, 2025 19:04

codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 3, 2025

HeshamHM28 closed this Oct 3, 2025

codeflash-ai bot deleted the codeflash/optimize-sorter-mgb7r1vd branch October 3, 2025 19:05

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⚡️ Speed up function `sorter` by 82% #793

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⚡️ Speed up function sorter by 82% #793

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codeflash-ai bot commented Oct 3, 2025

📄 82% (0.82x) speedup for sorter in code_to_optimize/bubble_sort.py

📝 Explanation and details

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⚡️ Speed up function `sorter` by 82% #793

⚡️ Speed up function `sorter` by 82% #793

📄 82% (0.82x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`