⚡️ Speed up function `sorter` by 182,233% #571

codeflash-ai · 2025-07-22T21:23:17Z

📄 182,233% (1,822.33x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

⏱️ Runtime : 3.36 seconds → 1.84 milliseconds (best of 564 runs)

📝 Explanation and details

Here's an optimized version of your program.
The original uses bubble sort, which is very inefficient.
Switch to Python's built-in list.sort(), which is much faster (Timsort, O(n log n)), and preserves the original function structure and output.

Comments have been preserved because only the sorting logic is replaced with a more efficient built-in operation.
The in-place behavior and returned value remain identical.
Output format is unchanged.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 55 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`	7.09ms	16.5μs	✅42866%
`test_bubble_sort.py::test_sort`	829ms	140μs	✅590026%
`test_bubble_sort_conditional.py::test_sort`	5.50μs	3.17μs	✅73.7%
`test_bubble_sort_import.py::test_sort`	837ms	134μs	✅623130%
`test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class`	829ms	134μs	✅616602%
`test_bubble_sort_parametrized.py::test_sort_parametrized`	508ms	132μs	✅384447%
`test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized`	101μs	21.3μs	✅376%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for maxsize in edge cases

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

# unit tests

# 1. 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 # 5.12μs -> 3.04μs (68.5% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 2.88μs (71.0% faster)

def test_sorter_basic_random():
    # Random order list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 2.92μs (61.5% faster)

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [3, 1, 2, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 2.96μs (63.4% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-2, 4, 0, -1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.08μs (55.4% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.96μs (26.8% faster)

def test_sorter_basic_two_elements():
    # List with two elements
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 2.88μs (42.0% faster)

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.04μs (61.6% faster)

def test_sorter_basic_floats():
    # List of floats
    arr = [3.2, 1.5, 2.8]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.54μs -> 3.54μs (56.5% faster)

def test_sorter_basic_mixed_int_float():
    # List of ints and floats
    arr = [2, 1.1, 3, 0.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 3.54μs (64.7% faster)

# 2. EDGE TEST CASES

def test_sorter_edge_empty():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.62μs -> 2.79μs (29.9% faster)

def test_sorter_edge_all_equal():
    # All elements equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 2.96μs (43.7% faster)

def test_sorter_edge_large_numbers():
    # Very large and very small integers
    arr = [sys.maxsize, -sys.maxsize - 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 3.21μs (55.9% faster)

def test_sorter_edge_large_floats():
    # Very large and very small floats
    arr = [1e308, -1e308, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.83μs -> 4.17μs (64.0% faster)

def test_sorter_edge_unicode_strings():
    # Unicode strings
    arr = ["éclair", "apple", "banana", "Éclair"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.96μs -> 3.42μs (74.4% faster)

def test_sorter_edge_case_sensitive_strings():
    # Case-sensitive string sorting
    arr = ["a", "B", "A", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.08μs (64.9% faster)

def test_sorter_edge_tuple_elements():
    # Sorting tuples by default (lexicographical order)
    arr = [(2, 3), (1, 2), (2, 2)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 3.92μs (37.2% faster)

def test_sorter_edge_custom_objects_fail():
    # Sorting custom objects without __lt__ should raise TypeError
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.88μs -> 1.88μs (53.3% faster)

def test_sorter_edge_none_values_fail():
    # Sorting list with None and ints should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 2.58μs -> 1.92μs (34.8% faster)

def test_sorter_edge_mutable_input():
    # Ensure input list is not mutated (sorter should not sort in-place)
    arr = [2, 1, 3]
    arr_copy = arr.copy()
    sorter(arr_copy) # 4.46μs -> 2.92μs (52.9% faster)

# 3. LARGE SCALE TEST CASES

def test_sorter_large_random_ints():
    # Large list of random integers
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.2ms -> 61.4μs (47427% 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 # 24.8ms -> 48.9μs (50541% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.7ms -> 28.8μs (64947% 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 # 31.4ms -> 28.6μs (109687% 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 # 30.3ms -> 91.1μs (33203% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 26.4ms -> 287μs (9105% faster)

def test_sorter_large_alternating():
    # Large list with alternating high/low values
    arr = [i if i % 2 == 0 else 999 - i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 25.6ms -> 50.4μs (50639% 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 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_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.12μs (49.3% faster)

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 2.92μs (70.0% faster)

def test_sorter_unsorted_list():
    # Unsorted list with positive integers
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 2.92μs (64.3% faster)

def test_sorter_with_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.12μs (65.3% faster)

def test_sorter_negative_numbers():
    # List with negative and positive numbers
    arr = [-1, -3, 2, 0, -2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.21μs (62.3% faster)

def test_sorter_single_element():
    # List with a single element should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.79μs -> 2.92μs (30.0% faster)

def test_sorter_two_elements():
    # Two elements, unsorted
    arr = [5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.92μs -> 2.92μs (34.2% faster)

def test_sorter_two_elements_sorted():
    # Two elements, already sorted
    arr = [2, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.88μs (30.4% faster)

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

def test_sorter_empty_list():
    # Empty list should return empty
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.42μs -> 2.79μs (22.4% faster)

def test_sorter_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.04μs (46.5% faster)

def test_sorter_large_negative_numbers():
    # Large negative numbers
    arr = [-1000000, -999999, -1000001, -1000000]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.42μs (50.0% faster)

def test_sorter_large_positive_numbers():
    # Large positive numbers
    arr = [1000000, 999999, 1000001, 1000000]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.29μs -> 3.17μs (35.6% faster)

def test_sorter_floats():
    # List with float values
    arr = [3.1, 2.4, -1.2, 0.0, 2.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.75μs -> 3.88μs (48.4% faster)

def test_sorter_mixed_int_float():
    # List with mixed int and float values
    arr = [2, 3.1, 1, 2.0, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.96μs -> 3.50μs (70.2% faster)

def test_sorter_strings():
    # List of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.17μs (55.3% faster)

def test_sorter_empty_strings():
    # List containing empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.08μs (60.8% faster)

def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.67μs -> 3.38μs (67.9% faster)

def test_sorter_single_character_strings():
    # List with single-character strings
    arr = ["d", "a", "c", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 3.04μs (57.5% 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()) # 2.54μs -> 1.96μs (29.8% faster)

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

def test_sorter_large_random_integers():
    # Large random list of integers (size 1000)
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.7ms -> 60.8μs (47011% faster)

def test_sorter_large_sorted():
    # Large already sorted list (size 1000)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.8ms -> 28.4μs (66080% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list (size 1000)
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.2ms -> 28.8μs (108207% 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 # 25.2ms -> 48.9μs (51506% 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 # 30.3ms -> 91.2μs (33113% 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 # 28.0ms -> 276μs (10002% faster)

# ------------------------
# Mutation Testing Guards
# ------------------------

def test_sorter_not_inplace():
    # Ensure that the function does not return a new list object but sorts in place
    arr = [2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 4.38μs -> 3.04μs (43.8% faster)

def test_sorter_stability():
    # Sorting should be stable: equal elements retain their original order
    class Obj:
        def __init__(self, key, idx):
            self.key = key
            self.idx = idx
        def __lt__(self, other):
            return self.key < other.key
        def __gt__(self, other):
            return self.key > other.key
        def __eq__(self, other):
            return self.key == other.key and self.idx == other.idx
        def __repr__(self):
            return f"Obj({self.key}, {self.idx})"
    a = Obj(1, 'a')
    b = Obj(1, 'b')
    c = Obj(2, 'c')
    arr = [c, a, b]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 4.17μs (46.0% faster)

def test_sorter_mutation_does_not_affect_input_copy():
    # Ensure that the original input is not mutated if a copy is passed
    arr = [4, 3, 2, 1]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr_copy); result = codeflash_output # 4.67μs -> 3.04μs (53.4% 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-mdf1k4jo and push.

Here's an optimized version of your program. The original uses bubble sort, which is very inefficient. Switch to Python's built-in `list.sort()`, which is much faster (Timsort, O(n log n)), and preserves the original function structure and output. - Comments have been preserved because only the sorting logic is replaced with a more efficient built-in operation. - The in-place behavior and returned value remain identical. - Output format is unchanged.

codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jul 22, 2025

codeflash-ai bot requested a review from aseembits93 July 22, 2025 21:23

misrasaurabh1 closed this Jul 24, 2025

codeflash-ai bot deleted the codeflash/optimize-sorter-mdf1k4jo branch July 24, 2025 20:54

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⚡️ Speed up function `sorter` by 182,233% #571

⚡️ Speed up function `sorter` by 182,233% #571

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⚡️ Speed up function sorter by 182,233% #571

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codeflash-ai bot commented Jul 22, 2025

📄 182,233% (1,822.33x) speedup for sorter in code_to_optimize/bubble_sort.py

📝 Explanation and details

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⚡️ Speed up function `sorter` by 182,233% #571

⚡️ Speed up function `sorter` by 182,233% #571

📄 182,233% (1,822.33x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`