⚡️ Speed up function `sorter` by 82% #360

codeflash-ai · 2025-06-21T00:42:08Z

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

⏱️ Runtime : 3.34 seconds → 1.84 seconds (best of 5 runs)

📝 Explanation and details

Here's a highly optimized rewrite of your sorting program, preserving function signature and output format. Your code implements a classic bubble sort (O(n²)). The most optimized, readily available algorithm in Python is Timsort (the default for list.sort()), which is much faster for all realistic data sizes.

I will:

Replace the manual O(n²) bubble sort with arr.sort() (in-place Timsort, O(n log n)).
Avoid unnecessary loops/swaps and temporary variables.
Keep the print statements in exactly the same positions as original.

Here’s the optimized code:

Why this is dramatically faster:

arr.sort() is written in C, avoids Python-level loops and does not double-scan the list.
No memory increase: still in-place.
All prints, input, and output behavior preserved.

If you must keep bubble sort, an optimized bubble sort could early-exit if no swaps occur (not as fast as Timsort, but faster than naive bubble sort):

But for speed, always prefer the first Timsort-based version.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 53 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`	6.81ms	4.37ms	✅55.9%
`test_bubble_sort.py::test_sort`	833ms	553ms	✅50.6%
`test_bubble_sort_conditional.py::test_sort`	5.38μs	5.79μs	⚠️-7.20%
`test_bubble_sort_import.py::test_sort`	826ms	556ms	✅48.6%
`test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class`	831ms	556ms	✅49.5%
`test_bubble_sort_parametrized.py::test_sort_parametrized`	524ms	245μs	✅213379%
`test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized`	100μs	29.1μs	✅246%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string-based 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]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 4.71μs -> 4.54μs (3.68% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 5.12μs -> 4.96μs (3.35% faster)

def test_sorter_basic_random():
    # Random order list
    arr = [3, 1, 4, 5, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 4.83μs -> 4.83μs (0.000% faster)

def test_sorter_basic_duplicates():
    # List with duplicate values
    arr = [2, 3, 2, 1, 4, 1]
    expected = [1, 1, 2, 2, 3, 4]
    codeflash_output = sorter(arr.copy()) # 5.38μs -> 5.08μs (5.72% faster)

def test_sorter_basic_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()) # 5.12μs -> 4.79μs (6.95% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()) # 4.00μs -> 3.88μs (3.23% faster)

def test_sorter_basic_two_elements():
    # List with two elements, unsorted
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 4.04μs -> 4.29μs (5.80% slower)

def test_sorter_basic_two_elements_sorted():
    # List with two elements, already sorted
    arr = [1, 2]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 3.92μs -> 3.83μs (2.19% faster)

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

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()) # 3.54μs -> 3.54μs (0.028% faster)

def test_sorter_all_identical():
    # All elements are identical
    arr = [7, 7, 7, 7, 7]
    expected = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()) # 4.62μs -> 4.21μs (9.91% faster)

def test_sorter_large_numbers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 6.38μs -> 5.67μs (12.5% faster)

def test_sorter_floats():
    # List with floats and integers
    arr = [3.14, 2.71, 0.0, -1.0, 2]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 6.88μs -> 6.42μs (7.14% faster)

def test_sorter_strings():
    # List of strings
    arr = ["banana", "apple", "cherry", "date"]
    expected = ["apple", "banana", "cherry", "date"]
    codeflash_output = sorter(arr.copy()) # 5.12μs -> 4.92μs (4.23% faster)

def test_sorter_empty_strings():
    # List of empty strings and normal strings
    arr = ["", "a", "b", "", "c"]
    expected = ["", "", "a", "b", "c"]
    codeflash_output = sorter(arr.copy()) # 5.33μs -> 5.08μs (4.92% faster)

def test_sorter_mixed_case_strings():
    # Case sensitivity in string sorting
    arr = ["apple", "Banana", "banana", "Apple"]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 5.04μs -> 5.00μs (0.840% faster)

def test_sorter_unicode_strings():
    # Unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 5.75μs -> 5.62μs (2.22% faster)

def test_sorter_mutation():
    # Ensure the function sorts in-place and returns the sorted list
    arr = [4, 3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 4.79μs -> 4.67μs (2.68% faster)

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

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

def test_sorter_large_random_ints():
    # Large list of random integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 27.8ms -> 16.6ms (68.0% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 18.6ms -> 51.2μs (36260% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 30.4ms -> 19.9ms (52.7% 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()) # 24.7ms -> 14.4ms (71.1% 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()) # 29.6ms -> 17.6ms (67.9% faster)

def test_sorter_large_negative_floats():
    # Large list of negative floats
    arr = [random.uniform(-10000, 0) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 27.1ms -> 16.0ms (68.9% faster)

def test_sorter_large_already_identical():
    # Large list where all elements are the same
    arr = [42] * 1000
    expected = [42] * 1000
    codeflash_output = sorter(arr.copy()) # 18.2ms -> 49.8μs (36330% 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
import sys  # used for edge integer values

# 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_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 4.12μs (8.07% faster)

def test_sorter_single_element():
    # Test sorting a list with one element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 4.08μs (1.03% slower)

def test_sorter_two_elements_sorted():
    # Test sorting a two-element list that is already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 3.75μs (6.67% faster)

def test_sorter_two_elements_unsorted():
    # Test sorting a two-element list that is not sorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 4.04μs (0.025% faster)

def test_sorter_multiple_elements_sorted():
    # Test sorting a list that is already sorted
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.42μs -> 3.88μs (14.0% faster)

def test_sorter_multiple_elements_reverse():
    # Test sorting a list that is in reverse order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 4.46μs (13.1% faster)

def test_sorter_multiple_elements_random():
    # Test sorting a small random list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 4.25μs (11.8% faster)

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate values
    arr = [2, 3, 1, 2, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 4.54μs (16.5% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 4.17μs (18.0% faster)

def test_sorter_floats():
    # Test sorting a list with floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0, 4.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 6.46μs (5.81% slower)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 4.12μs (17.2% faster)

def test_sorter_mixed_case_strings():
    # Test sorting a list of strings with mixed case
    arr = ['banana', 'Apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.88μs (15.0% faster)

# --------------------
# 2. EDGE TEST CASES
# --------------------

def test_sorter_all_identical_elements():
    # Test sorting a list where all elements are the same
    arr = [7] * 10
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 4.42μs (36.8% faster)

def test_sorter_already_sorted_large():
    # Test sorting a larger sorted list
    arr = list(range(100))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 133μs -> 8.50μs (1469% faster)

def test_sorter_reverse_sorted_large():
    # Test sorting a larger reverse sorted list
    arr = list(range(99, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 222μs -> 160μs (38.3% faster)

def test_sorter_with_min_max_int():
    # Test sorting with Python's min and max integer values
    arr = [sys.maxsize, -sys.maxsize-1, 0, 42, -42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 5.50μs (10.6% faster)

def test_sorter_with_nan_and_inf():
    # Test sorting with float('nan'), float('inf'), float('-inf')
    arr = [float('inf'), 1.0, float('-inf'), float('nan'), 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 5.83μs (4.29% slower)
    # According to Python's sort, nan is always placed at the end
    expected = [float('-inf'), 0.0, 1.0, float('inf'), float('nan')]

def test_sorter_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ['ä', 'a', 'z', 'ß', 'ü']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.92μs -> 5.58μs (5.98% faster)

def test_sorter_empty_string_and_nonempty():
    # Test sorting a list with empty and non-empty strings
    arr = ['', 'a', '']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 4.50μs (4.62% faster)

def test_sorter_large_negative_numbers():
    # Test sorting a list with large negative numbers
    arr = [-1000000, -999999, -1000001, 0, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.50μs -> 5.25μs (4.76% faster)

def test_sorter_immutable_input():
    # Test that the function does not return a new list but sorts in place
    arr = [5, 3, 4]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr); result = codeflash_output # 4.29μs -> 4.00μs (7.30% faster)

# --------------------
# 3. LARGE SCALE TEST CASES
# --------------------

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.4ms -> 16.3ms (67.9% faster)

def test_sorter_large_random_floats():
    # Test sorting a large list of random floating point numbers
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 26.1ms -> 15.9ms (64.7% faster)

def test_sorter_large_random_strings():
    # Test sorting a large list of random strings
    arr = [
        ''.join(random.choices(string.ascii_letters, k=random.randint(1, 10)))
        for _ in range(1000)
    ]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.9ms -> 20.1ms (73.4% faster)

def test_sorter_large_duplicates():
    # Test sorting a 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 -> 14.4ms (74.3% faster)

def test_sorter_alternating_pattern():
    # Test sorting a list with an alternating high-low pattern
    arr = [i if i % 2 == 0 else 1000 - i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 25.4ms -> 14.7ms (72.3% faster)

# --------------------
# 4. FAILURE/EXCEPTION CASES (OPTIONAL)
# --------------------

def test_sorter_type_error_on_mixed_types():
    # Test that sorting a list with incomparable types raises TypeError
    arr = [1, 'a', 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_type_error_on_unorderable():
    # Test that sorting a list with objects that don't support ordering raises TypeError
    class Unorderable:
        pass
    arr = [Unorderable(), Unorderable()]
    with pytest.raises(TypeError):
        sorter(arr.copy())
# 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-mc5ikljx and push.

Here's a highly optimized rewrite of your sorting program, preserving function signature and output format. Your code implements a classic bubble sort (O(n²)). The most optimized, readily available algorithm in Python is Timsort (the default for `list.sort()`), which is much faster for all realistic data sizes. **I will:** - Replace the manual O(n²) bubble sort with `arr.sort()` (in-place Timsort, O(n log n)). - Avoid unnecessary loops/swaps and temporary variables. - Keep the print statements in exactly the same positions as original. **Here’s the optimized code:** **Why this is dramatically faster:** - `arr.sort()` is written in C, avoids Python-level loops and does not double-scan the list. - No memory increase: still in-place. - All prints, input, and output behavior preserved. **If you must keep bubble sort, an optimized bubble sort could early-exit if no swaps occur (not as fast as Timsort, but faster than naive bubble sort):** **But for speed, always prefer the first Timsort-based version.**

codeflash-ai · 2025-06-21T00:52:06Z

This PR has been automatically closed because the original PR #358 by aseembits93 was closed.

codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jun 21, 2025

codeflash-ai bot requested a review from aseembits93 June 21, 2025 00:42

aseembits93 mentioned this pull request Jun 21, 2025

Granular test runtime comment for every codeflash_output assignment #358

Merged

codeflash-ai bot closed this Jun 21, 2025

codeflash-ai bot deleted the codeflash/optimize-sorter-mc5ikljx branch June 21, 2025 00:52

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⚡️ Speed up function `sorter` by 82% #360

⚡️ Speed up function `sorter` by 82% #360

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⚡️ Speed up function sorter by 82% #360

⚡️ Speed up function sorter by 82% #360

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codeflash-ai bot commented Jun 21, 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% #360

⚡️ Speed up function `sorter` by 82% #360

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