⚡️ Speed up function `sorter` by 205,326% #589

codeflash-ai · 2025-07-28T21:06:40Z

📄 205,326% (2,053.26x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

⏱️ Runtime : 3.33 seconds → 1.62 milliseconds (best of 594 runs)

📝 Explanation and details

Looking at the code, I can see that the optimization from bubble sort to Python's built-in sort() is excellent and should be preserved. However, I notice that there's an added comment in the optimized code that wasn't in the original. According to the refinement rules, I should revert comments that are different from the original unless the new code is complex and requires additional context.

Since arr.sort() is a simple, well-known Python method that doesn't require additional explanation, I'll remove the comment to make the optimization more precise and closer to the original code structure.

<replace_in_file>
optimized_source_code

<<<<<<< SEARCH
arr.sort() # Use Python's built-in efficient sort

arr.sort()

REPLACE

The refined optimization maintains the core performance improvement (replacing O(n²) bubble sort with O(n log n) built-in sort) while removing the unnecessary comment to keep the code closer to the original structure. This makes the optimization more precise with fewer character differences from the original code.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 52 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`	6.97ms	16.6μs	✅41812%
`test_bubble_sort.py::test_sort`	826ms	140μs	✅586547%
`test_bubble_sort_conditional.py::test_sort`	5.29μs	3.17μs	✅67.1%
`test_bubble_sort_import.py::test_sort`	827ms	143μs	✅578707%
`test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class`	828ms	142μs	✅582238%
`test_bubble_sort_parametrized.py::test_sort_parametrized`	506ms	140μs	✅359573%
`test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized`	103μs	21.1μs	✅390%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for generating lists of strings
import sys  # used for maxsize edge case

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

# unit tests

# ----------------------
# Basic Test Cases
# ----------------------

def test_sorter_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.08μs (50.0% faster)

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.08μs (64.9% faster)

def test_sorter_unsorted_list():
    # Unsorted list should be sorted
    arr = [3, 1, 4, 5, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.04μs (58.9% faster)

def test_sorter_list_with_duplicates():
    # List with duplicate values
    arr = [4, 2, 2, 3, 1, 4]
    expected = [1, 2, 2, 3, 4, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.12μs (56.0% faster)

def test_sorter_list_with_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    expected = [-3, -2, -1, 0, 1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.29μs (39.3% faster)

def test_sorter_list_with_floats():
    # List with floats and integers
    arr = [3.5, 2, 4.2, 1.1, 2]
    expected = [1.1, 2, 2, 3.5, 4.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.08μs -> 4.00μs (77.1% faster)

def test_sorter_list_with_single_element():
    # List with a single element
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.71μs -> 3.00μs (23.6% faster)

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.29μs -> 2.88μs (14.5% faster)

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

def test_sorter_all_elements_equal():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    expected = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.21μs -> 3.04μs (38.4% faster)

def test_sorter_large_and_small_integers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 1, -1]
    expected = [-sys.maxsize-1, -1, 0, 1, sys.maxsize]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 3.38μs (72.9% faster)

def test_sorter_list_with_strings():
    # List of strings should be sorted lexicographically
    arr = ['banana', 'apple', 'cherry', 'date']
    expected = ['apple', 'banana', 'cherry', 'date']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.29μs (35.4% faster)

def test_sorter_list_with_empty_strings():
    # List with empty strings and normal strings
    arr = ['', 'a', 'abc', '', 'b']
    expected = ['', '', 'a', 'abc', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.38μs (48.1% faster)

def test_sorter_list_with_booleans():
    # List with booleans (False < True)
    arr = [True, False, True, False]
    expected = [False, False, True, True]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.42μs -> 3.29μs (34.2% faster)

def test_sorter_list_with_mixed_types_raises():
    # List with mixed types (e.g., int and str) should raise TypeError
    arr = [1, 'a', 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.67μs -> 1.92μs (39.2% faster)

def test_sorter_list_with_none_raises():
    # List with None and numbers should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.33μs -> 1.92μs (21.7% faster)

def test_sorter_list_with_nested_lists_raises():
    # List with nested lists should raise TypeError
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.54μs -> 1.79μs (41.9% faster)

def test_sorter_list_with_nan_and_inf():
    # List with float('nan'), float('inf'), float('-inf')
    arr = [float('nan'), 1, float('inf'), float('-inf'), 0]
    # Sorting with nan is tricky; nan is always placed at the end in Python's sort
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.25μs -> 3.46μs (80.7% faster)

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

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-100000, -99000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.0ms -> 61.9μs (45066% faster)

def test_sorter_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 29.0μs (63741% faster)

def test_sorter_large_reverse_sorted_list():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.8ms -> 28.8μs (106898% faster)

def test_sorter_large_list_with_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 # 25.0ms -> 47.0μs (53065% faster)

def test_sorter_large_list_of_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 # 30.3ms -> 94.8μs (31862% faster)

def test_sorter_large_list_all_equal():
    # Large list where all elements are the same
    arr = [42] * 1000
    expected = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.2ms -> 27.9μs (65056% faster)

def test_sorter_large_list_with_negatives_and_positives():
    # Large list with both negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.4ms -> 64.4μs (44057% faster)

# ----------------------
# In-place vs Return Value Test
# ----------------------

def test_sorter_returns_same_object():
    # sorter should sort in-place and return the same object
    arr = [3, 2, 1]
    id_before = id(arr)
    codeflash_output = sorter(arr); result = codeflash_output # 4.67μs -> 3.04μs (53.5% faster)
    id_after = id(result)
# 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 scale test cases
import string  # used for string sorting test cases
import sys  # used for testing with large/small numbers

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

# unit tests

# ------------------- Basic Test Cases -------------------

def test_sorter_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.17μs (59.2% faster)

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.12μs (62.7% faster)

def test_sorter_unsorted_list():
    # Unsorted list with random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.00μs (66.7% faster)

def test_sorter_duplicates():
    # List with duplicate elements
    arr = [4, 2, 5, 2, 3, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.79μs -> 3.17μs (82.9% faster)

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.17μs (57.9% faster)

def test_sorter_mixed_positive_negative():
    # List with both positive and negative numbers
    arr = [3, -2, 0, 1, -5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.21μs (39.0% faster)

def test_sorter_single_element():
    # List with a single element should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.96μs (31.0% faster)

def test_sorter_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.96μs (31.0% faster)

def test_sorter_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.96μs (32.4% faster)

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

def test_sorter_empty_list():
    # Empty list should return an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.50μs -> 2.83μs (23.5% faster)

def test_sorter_all_identical():
    # All elements are the same
    arr = [7] * 10
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.96μs -> 3.17μs (88.1% faster)

def test_sorter_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.38μs -> 3.46μs (84.3% faster)

def test_sorter_floats():
    # List with floating point numbers
    arr = [3.14, 2.71, -1.0, 0.0, 2.71]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.50μs -> 3.67μs (77.3% faster)

def test_sorter_strings():
    # List of strings (should be sorted lexicographically)
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.33μs (50.0% faster)

def test_sorter_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.17μs (57.9% faster)

def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 3.67μs (67.0% faster)

def test_sorter_mixed_types_raises():
    # List with mixed types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.75μs -> 1.83μs (49.9% faster)

def test_sorter_nested_lists_raises():
    # List with nested lists should raise TypeError
    arr = [1, [2, 3], 4]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.67μs -> 1.83μs (45.4% 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 # 28.5ms -> 61.0μs (46600% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 29.1μs (63609% 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 # 30.7ms -> 29.0μs (105885% 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 # 25.0ms -> 49.3μs (50486% 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 # 29.7ms -> 93.2μs (31790% faster)

def test_sorter_large_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 # 27.6ms -> 281μs (9725% faster)

# ------------------- Mutation Testing Protection -------------------

def test_sorter_mutation_protection():
    # Ensure that the function does not return a new list, but sorts in-place
    arr = [3, 2, 1]
    arr_id = id(arr)
    codeflash_output = sorter(arr); result = codeflash_output # 4.83μs -> 3.17μs (52.6% faster)

def test_sorter_does_not_modify_input_when_empty():
    # Ensure that empty input is not mutated in any way
    arr = []
    codeflash_output = sorter(arr); result = codeflash_output # 3.50μs -> 2.88μs (21.7% faster)

def test_sorter_stability():
    # Stability: elements with equal keys should retain their relative order
    class Item:
        def __init__(self, key, value):
            self.key = key
            self.value = value
        def __lt__(self, other):
            return self.key < other.key
        def __eq__(self, other):
            return self.key == other.key and self.value == other.value
        def __repr__(self):
            return f"Item({self.key}, {self.value})"
    arr = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c'), Item(2, 'd')]
    expected = [arr[0], arr[2], arr[1], arr[3]]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.92μs -> 4.21μs (88.1% 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-mdnllvii and push.

Looking at the code, I can see that the optimization from bubble sort to Python's built-in `sort()` is excellent and should be preserved. However, I notice that there's an added comment in the optimized code that wasn't in the original. According to the refinement rules, I should revert comments that are different from the original unless the new code is complex and requires additional context. Since `arr.sort()` is a simple, well-known Python method that doesn't require additional explanation, I'll remove the comment to make the optimization more precise and closer to the original code structure. <replace_in_file> <path>optimized_source_code</path> <diff> <<<<<<< SEARCH arr.sort() # Use Python's built-in efficient sort ======= arr.sort() >>>>>>> REPLACE </diff> </replace_in_file> The refined optimization maintains the core performance improvement (replacing O(n²) bubble sort with O(n log n) built-in sort) while removing the unnecessary comment to keep the code closer to the original structure. This makes the optimization more precise with fewer character differences from the original code.

codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jul 28, 2025

codeflash-ai bot requested a review from aseembits93 July 28, 2025 21:06

aseembits93 closed this Jul 28, 2025

codeflash-ai bot deleted the codeflash/optimize-sorter-mdnllvii branch July 28, 2025 21:13

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⚡️ Speed up function `sorter` by 205,326% #589

⚡️ Speed up function `sorter` by 205,326% #589

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⚡️ Speed up function sorter by 205,326% #589

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codeflash-ai bot commented Jul 28, 2025

📄 205,326% (2,053.26x) speedup for sorter in code_to_optimize/bubble_sort.py

📝 Explanation and details

<replace_in_file> optimized_source_code <<<<<<< SEARCH arr.sort() # Use Python's built-in efficient sort

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⚡️ Speed up function `sorter` by 205,326% #589

⚡️ Speed up function `sorter` by 205,326% #589

📄 205,326% (2,053.26x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

<replace_in_file>
optimized_source_code

<<<<<<< SEARCH
arr.sort() # Use Python's built-in efficient sort