⚡️ Speed up function `sorter` by 174,908% #344

codeflash-ai · 2025-06-18T01:37:50Z

📄 174,908% (1,749.08x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

⏱️ Runtime : 3.39 seconds → 1.94 milliseconds (best of 491 runs)

📝 Explanation and details

Here’s your optimized program. The original code uses an unoptimized bubble sort (O(n²)), always iterating through the entire list regardless of whether it's already sorted.
A faster approach is to use Python’s built-in sort method (Timsort, O(n log n)), which is highly optimized.
I’ve preserved your print statements and function signature.

Comments:

The purpose and behavior (including print output) remain identical.
This approach is significantly faster for all but the smallest lists.
If you require sorting without mutating the input list, use sorted(arr) (returns a new list).
Let me know if you need to preserve the original or add more features!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 54 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details and Runtime

🧪Test File	🧬Test Name	⏱️Original	⏱️Optimized	💯Gain
`benchmarks/test_benchmark_bubble_sort.py`	`test_sort2`	`6.97ms`	`16.5μs`	`✅42042.47%`
`test_bubble_sort.py`	`test_sort`	`825ms`	`139μs`	`✅592141.14%`
`test_bubble_sort_conditional.py`	`test_sort`	`6.00μs`	`2.96μs`	`✅102.84%`
`test_bubble_sort_import.py`	`test_sort`	`832ms`	`141μs`	`✅588624.04%`
`test_bubble_sort_in_class.py`	`TestSorter.test_sort_in_pytest_class`	`831ms`	`138μs`	`✅602634.76%`
`test_bubble_sort_parametrized.py`	`test_sort_parametrized`	`507ms`	`136μs`	`✅371076.70%`
`test_bubble_sort_parametrized_loop.py`	`test_sort_loop_parametrized`	`95.8μs`	`20.5μs`	`✅366.60%`

🌀 Generated Regression Tests Details and Runtime

import random  # used for generating large scale test data
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

# --- 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 # 4.50μs -> 3.00μs (50.00%)

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 # 5.04μs -> 2.92μs (72.81%)

def test_sorter_basic_unsorted():
    # Unsorted list with random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 2.92μs (64.33%)

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [4, 2, 2, 5, 3, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.04μs (67.15%)

def test_sorter_basic_negatives():
    # List with negative and positive numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.04μs (58.93%)

def test_sorter_basic_single_element():
    # List with a single element should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.62μs -> 2.83μs (27.91%)

def test_sorter_basic_two_elements():
    # Two-element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.75μs -> 2.83μs (32.37%)

def test_sorter_basic_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 2.92μs (37.13%)

# --- EDGE TEST CASES ---

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.42μs -> 2.75μs (24.22%)

def test_sorter_large_integers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 1, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 3.46μs (74.67%)

def test_sorter_floats():
    # List with floats and integers
    arr = [3.2, 1.5, 2, 4.8, 2.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.83μs -> 3.88μs (76.36%)

def test_sorter_strings():
    # List of strings should sort lexicographically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.08μs (52.71%)

def test_sorter_mixed_types_raises():
    # List with mixed types should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_preserves_input_type():
    # Ensure the function returns a list, not another type
    arr = [3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 2.92μs (50.03%)


def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["éclair", "apple", "zèbre"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 3.54μs (70.58%)
    # Note: Python's default sort is locale-independent, so accents sort after ASCII.

def test_sorter_boolean_values():
    # List with boolean values (False < True)
    arr = [True, False, True]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.00μs (48.63%)

def test_sorter_mutates_input():
    # The function should mutate the input list (since it sorts in-place)
    arr = [3, 1, 2]
    sorter(arr)

# --- LARGE SCALE TEST CASES ---

def test_sorter_large_random_integers():
    # Sorting a large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.7ms -> 61.8μs (46340.14%)

def test_sorter_large_sorted():
    # Sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.8ms -> 29.5μs (63417.93%)

def test_sorter_large_reverse_sorted():
    # Sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.1ms -> 29.5μs (105352.97%)

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 -> 51.2μs (48819.39%)

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 # 30.1ms -> 92.4μs (32499.14%)

def test_sorter_large_all_equal():
    # Large list with all elements equal
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 17.6ms -> 27.9μs (63217.04%)

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 # 26.2ms -> 286μs (9057.21%)
# 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

# ----------------------
# 1. 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 # 5.12μs -> 3.04μs (68.53%)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 2.96μs (78.87%)

def test_sorter_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.04μs (61.66%)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 2.88μs (65.22%)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.04μs (67.09%)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.79μs -> 2.75μs (37.89%)

def test_sorter_basic_two_elements():
    # Two element list
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 2.92μs (40.05%)

def test_sorter_basic_all_same():
    # All elements the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 2.92μs (48.58%)

def test_sorter_basic_floats():
    # List with floats
    arr = [2.2, 1.1, 3.3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.25μs -> 3.46μs (51.82%)

def test_sorter_basic_mixed_int_float():
    # List with ints and floats
    arr = [1, 2.2, 0.5, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.79μs -> 3.50μs (65.46%)

# ----------------------
# 2. Edge Test Cases
# ----------------------

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.08μs -> 2.71μs (13.85%)

def test_sorter_edge_large_negative():
    # List with large negative and positive numbers
    arr = [1000000, -1000000, 0, 999999, -999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.67μs -> 3.33μs (69.95%)

def test_sorter_edge_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.04μs (60.26%)

def test_sorter_edge_strings_case():
    # List of strings with different cases
    arr = ["banana", "Apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 2.96μs (47.90%)

def test_sorter_edge_unicode_strings():
    # List of unicode strings
    arr = ["éclair", "apple", "Éclair"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.29μs (58.20%)

def test_sorter_edge_min_max_int():
    # List with min and max int
    arr = [-(2**31), 0, 2**31 - 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.54μs -> 3.04μs (49.31%)

def test_sorter_edge_min_max_float():
    # List with min and max float
    arr = [float('inf'), float('-inf'), 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.29μs (49.33%)

def test_sorter_edge_nan():
    # List with NaN (should place NaN at the end, as per Python's sort)
    arr = [3, float('nan'), 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.17μs (44.71%)

def test_sorter_edge_large_and_small_floats():
    # List with very large and very small floats
    arr = [1e308, 1e-308, -1e308, -1e-308]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.71μs -> 5.29μs (64.57%)

def test_sorter_edge_custom_objects():
    # List of custom objects with __lt__ defined
    class Box:
        def __init__(self, value):
            self.value = value
        def __lt__(self, other):
            return self.value < other.value
        def __eq__(self, other):
            return self.value == other.value
        def __repr__(self):
            return f"Box({self.value})"
    arr = [Box(3), Box(1), Box(2)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.25μs -> 3.79μs (64.86%)

def test_sorter_edge_mutation():
    # Ensure function mutates the list in-place
    arr = [2, 1]
    sorter(arr)

def test_sorter_edge_type_error():
    # List with incomparable types should raise TypeError
    arr = [1, "a"]
    with pytest.raises(TypeError):
        sorter(arr.copy())

# ----------------------
# 3. 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 # 18.4ms -> 30.0μs (61464.97%)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.8ms -> 29.7μs (103874.95%)

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 # 29.2ms -> 59.8μs (48745.95%)

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 # 24.6ms -> 51.0μs (48158.65%)

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 -> 89.9μs (32972.32%)

def test_sorter_large_negative_positive():
    # Large list with negative and positive numbers
    arr = [random.randint(-100000, 100000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.2ms -> 66.8μs (42107.61%)

def test_sorter_large_all_same():
    # Large list with all elements the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.1ms -> 27.5μs (65737.40%)

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 # 26.0ms -> 286μs (8990.26%)
# 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-mc1a8nze and push.

Here’s your optimized program. The original code uses an unoptimized bubble sort (O(n²)), always iterating through the entire list regardless of whether it's already sorted. A faster approach is to use Python’s built-in `sort` method (Timsort, O(n log n)), which is highly optimized. I’ve preserved your print statements and function signature. **Comments:** - The purpose and behavior (including print output) remain identical. - This approach is significantly faster for all but the smallest lists. - If you require sorting **without mutating** the input list, use `sorted(arr)` (returns a new list). Let me know if you need to preserve the original or add more features!

aseembits93 · 2025-06-18T01:42:00Z

Test File	Test Name	Original time	Optimized time	Percentage Gain
`benchmarks/test_benchmark_bubble_sort.py::test_sort2`	`test_sort2`	6.97ms	16.5μs	✅42042.47%
`test_bubble_sort.py`	`test_sort`	`825ms`	`139μs`	`✅592141.14%`
`test_bubble_sort_conditional.py`	`test_sort`	`6.00μs`	`2.96μs`	`✅102.84%`
`test_bubble_sort_import.py`	`test_sort`	`832ms`	`141μs`	`✅588624.04%`
`test_bubble_sort_in_class.py`	`TestSorter.test_sort_in_pytest_class`	`831ms`	`138μs`	`✅602634.76%`
`test_bubble_sort_parametrized.py`	`test_sort_parametrized`	`507ms`	`136μs`	`✅371076.70%`
`test_bubble_sort_parametrized_loop.py`	`test_sort_loop_parametrized`	`95.8μs`	`20.5μs`	`✅366.60%`

aseembits93 · 2025-06-18T01:46:55Z

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
`benchmarks/test_benchmark_bubble_sort.py::test_sort2`	6.97ms	16.5μs	✅42042.47%
`benchmarks/test_benchmark_bubble_sort.py::test_sort2`	6.97ms	16.5μs	⚠️-42042.47%
`benchmarks/test_benchmark_bubble_sort.py::test_sort2`	6.97ms	NaN	❌

codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jun 18, 2025

codeflash-ai bot requested a review from aseembits93 June 18, 2025 01:37

aseembits93 closed this Jun 18, 2025

codeflash-ai bot deleted the codeflash/optimize-sorter-mc1a8nze branch June 18, 2025 03:15

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⚡️ Speed up function `sorter` by 174,908% #344

⚡️ Speed up function `sorter` by 174,908% #344

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codeflash-ai bot commented Jun 18, 2025

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aseembits93 commented Jun 18, 2025 •

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aseembits93 commented Jun 18, 2025 •

edited by misrasaurabh1

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⚡️ Speed up function sorter by 174,908% #344

⚡️ Speed up function sorter by 174,908% #344

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codeflash-ai bot commented Jun 18, 2025

📄 174,908% (1,749.08x) speedup for sorter in code_to_optimize/bubble_sort.py

📝 Explanation and details

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⚡️ Speed up function `sorter` by 174,908% #344

⚡️ Speed up function `sorter` by 174,908% #344

📄 174,908% (1,749.08x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

aseembits93 commented Jun 18, 2025 •

edited

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aseembits93 commented Jun 18, 2025 •

edited by misrasaurabh1

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