⚡️ Speed up function sorter by 80%

[codeflash-ai]

📄 80% (0.80x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.34 seconds → 1.85 seconds (best of 5 runs)

📝 Explanation and details

Here is a greatly optimized version of your sorting function. You are currently using an unoptimized bubble sort, which is both time- and cache-inefficient for large lists. There are several ways to improve its performance without changing the function signature or output.

• Use Python's built-in sort(), which is highly optimized (Timsort; O(n log n)).
• If you must keep the sorting "manual", at least optimize bubble sort by adding an "early exit" if no swaps are made, as well as shrinking the unsorted region each pass.
• Avoid repeated len(arr) calls in the loop.

Below, option 1 uses built-in sort (fastest in practice), and option 2 is an optimized in-place bubble sort in case you need to keep the bubble sort code style.

---

Option 1: Use Python’s built-in sort (Recommended, unless manual sort required)

---

Option 2: Optimized Bubble Sort (If you want to keep the basic algorithm)

Notes on optimization:

• Early exit: If no swaps, stop early (best-case O(n)).
• Avoid unnecessary work: Don't recheck sorted tail.
• Tuple swap: Pythonic, can be faster than three assignments.

---

Recommendation

• If you only care about speed and not the sorting algorithm: Use Option 1.
• If you're required to use bubble sort or similar: Use Option 2.

Either will be vastly faster than your original code.
Your main slowness was due to the O(n²) inefficient bubble sort.

Let me know if you need it adapted for e.g. descending order or for specific data types!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 59 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details and Performance Breakdown

• benchmarks/test_benchmark_bubble_sort.py

  • test_sort2: 7.10ms -> 4.33ms $\color{green}(64\%)$

• test_bubble_sort.py

  • test_sort: 810ms -> 549ms $\color{green}(0.47\%)$

• test_bubble_sort_conditional.py

  • test_sort: 5.62μs -> 5.46μs $\color{green}(0.03\%)$

• test_bubble_sort_import.py

  • test_sort: 817ms -> 554ms $\color{green}(0.47\%)$

• test_bubble_sort_in_class.py

  • TestSorter.test_sort_in_pytest_class: 820ms -> 550ms $\color{green}(0.49\%)$

• test_bubble_sort_parametrized.py

  • test_sort_parametrized: 498ms -> 248μs $\color{green}(2005.50\%)$

• test_bubble_sort_parametrized_loop.py

  • test_sort_loop_parametrized: 94.8μs -> 27.5μs $\color{green}(2.45\%)$

Test File	Test Name	Before	After	Improvement
benchmarks/test_benchmark_bubble_sort.py	test_sort2	7.10 ms	4.33 ms	$\color{green}(0.47\%)$
test_bubble_sort.py	test_sort	810 ms	549 ms	$\color{green}(0.47\%)$
test_bubble_sort_conditional.py	test_sort	5.62 μs	5.46 μs	$\color{green}(0.03\%)$
test_bubble_sort_import.py	test_sort	817 ms	554 ms	$\color{green}(0.47\%)$
test_bubble_sort_in_class.py	TestSorter.test_sort_in_pytest_class	820 ms	550 ms	$\color{green}(0.49\%)$
test_bubble_sort_parametrized.py	test_sort_parametrized	498 ms	248 μs	$\color{green}(2005.50\%)$
test_bubble_sort_parametrized_loop.py	test_sort_loop_parametrized	94.8 μs	27.5 μs	$\color{green}(2.45\%)$

📊 Bubble Sort Benchmark Improvements

🧪 Test File	🧬 Test Name	⏱️ Before	⏱️ After	📈 Improvement
benchmarks/test_benchmark_bubble_sort.py	test_sort2	7.10 ms	4.33 ms	✅ $\color{green}64\%$
test_bubble_sort.py	test_sort	810 ms	549 ms	✅ $\color{green}0.47\%$
test_bubble_sort_conditional.py	test_sort	5.62 μs	5.46 μs	✅ $\color{green}0.03\%$
test_bubble_sort_import.py	test_sort	817 ms	554 ms	✅ $\color{green}0.47\%$
test_bubble_sort_in_class.py	TestSorter.test_sort_in_pytest_class	820 ms	550 ms	✅ $\color{green}0.49\%$
test_bubble_sort_parametrized.py	test_sort_parametrized	498 ms	248 μs	🚀 $\color{green}2005.50\%$
test_bubble_sort_parametrized_loop.py	test_sort_loop_parametrized	94.8 μs	27.5 μs	🚀 $\color{green}2.45\%$

🌀 Generated Regression Tests Details and Performance Breakdown

import random  # used for generating large test cases
import string  # used for string sorting tests
import sys  # used for special value 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_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 3.71μs (0.10%)

def test_sorter_single_element():
    # Test sorting a single-element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 3.96μs (0.07%)

def test_sorter_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 4.25μs (0.13%)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse-sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 5.04μs (-0.04%)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    arr = [3, 1, 4, 2, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 4.71μs (0.06%)

def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 4.79μs (-0.07%)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-2, -5, 0, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.21μs -> 4.62μs (-0.09%)

def test_sorter_all_equal():
    # Test sorting a list where all elements are equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.83μs -> 4.12μs (-0.07%)

def test_sorter_floats():
    # Test sorting a list with floating point numbers
    arr = [3.2, 1.5, 2.8, 1.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.88μs -> 5.46μs (0.08%)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 4.58μs (0.04%)

def test_sorter_mixed_case_strings():
    # Test sorting a list of strings with mixed cases
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 4.62μs (-0.03%)

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

def test_sorter_large_negative_and_positive():
    # Test sorting a list with large negative and positive numbers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999, -999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.17μs -> 6.00μs (0.03%)

def test_sorter_with_inf_and_nan():
    # Test sorting a list with float('inf'), float('-inf'), and float('nan')
    arr = [float('inf'), 1.0, float('-inf'), float('nan'), 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.75μs -> 5.38μs (0.07%)

def test_sorter_already_sorted_large():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.3ms -> 51.3μs (355.18%)

def test_sorter_reverse_sorted_large():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.0ms -> 19.8ms (0.56%)

def test_sorter_all_same_large():
    # Test sorting a large list with all the same value
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 17.6ms -> 49.5μs (355.20%)

def test_sorter_minimal_and_maximal_ints():
    # Test sorting a list with Python's min and max integer values
    arr = [0, sys.maxsize, -sys.maxsize-1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.25μs -> 5.04μs (0.04%)

def test_sorter_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.25μs -> 5.75μs (0.09%)

def test_sorter_empty_strings():
    # Test sorting a list with empty strings
    arr = ["", "a", "", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.79μs -> 4.67μs (0.03%)

def test_sorter_boolean_values():
    # Test sorting a list of boolean values
    arr = [True, False, True, False]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 4.79μs (0.03%)

def test_sorter_mutation():
    # Test that the function mutates the input list (since it sorts in-place)
    arr = [3, 2, 1]
    sorter(arr)

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

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = [random.randint(-100000, 100000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.7ms -> 16.6ms (0.67%)

def test_sorter_large_random_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 25.9ms -> 16.0ms (0.62%)

def test_sorter_large_random_strings():
    # Test sorting a large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=8)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.1ms -> 17.2ms (0.70%)

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 # 24.6ms -> 14.2ms (0.73%)

def test_sorter_large_alternating():
    # Test sorting a large list with alternating pattern
    arr = [i % 2 for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 21.2ms -> 9.65ms (1.19%)

# -------------------- Miscellaneous/Robustness --------------------

def test_sorter_type_error_on_mixed_types():
    # Test that sorting a list with mixed incomparable types raises TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_type_error_on_unorderable():
    # Test that sorting a list with unorderable types raises TypeError
    arr = [object(), object()]
    # All objects are unorderable unless __lt__ is defined
    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.

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for maxsize/minsize 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 # 5.42μs -> 4.25μs (0.27%)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted ascendingly
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 5.21μs (-0.09%)

def test_sorter_basic_unsorted():
    # Unsorted list with unique integers
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 4.79μs (-0.09%)

def test_sorter_basic_duplicates():
    # List with duplicate values
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.33μs -> 4.67μs (-0.07%)

def test_sorter_basic_single_element():
    # Single-element list should return the same element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.71μs -> 4.00μs (-0.07%)

def test_sorter_basic_two_elements():
    # Two-element list, unsorted
    arr = [7, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.79μs -> 4.25μs (-0.11%)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [0, -1, -3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 4.96μs (-0.12%)

def test_sorter_basic_mixed_signs():
    # List with both positive and negative numbers
    arr = [-10, 5, 0, -2, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 4.92μs (0.04%)

def test_sorter_basic_floats():
    # List with float values
    arr = [3.1, 2.4, 5.6, 1.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.79μs -> 5.62μs (0.03%)

def test_sorter_basic_mixed_int_float():
    # List with both int and float values
    arr = [3, 1.5, 2, 4.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 5.21μs (-0.05%)

def test_sorter_basic_strings():
    # List of strings should be sorted lexicographically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.17μs -> 4.71μs (-0.12%)

def test_sorter_basic_strings_case():
    # Strings with different cases
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 4.46μs (-0.11%)

def test_sorter_basic_empty():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.38μs -> 3.83μs (-0.12%)

# --- Edge Test Cases ---

def test_sorter_edge_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 4.21μs (-0.08%)

def test_sorter_edge_large_numbers():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.88μs -> 6.12μs (-0.04%)

def test_sorter_edge_large_floats():
    # List with very large and very small floats
    arr = [1e308, -1e308, 0.0, 1.5e307, -1.5e307]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.67μs -> 7.88μs (0.10%)

def test_sorter_edge_nan_inf():
    # List with float('nan'), float('inf'), float('-inf')
    arr = [float('nan'), float('inf'), float('-inf'), 0.0]
    # Sorting with NaN is special: NaN is not equal to anything, including itself.
    # Python's sort puts NaN at the end.
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 4.83μs (-0.08%)

def test_sorter_edge_empty_string():
    # List with empty string and other strings
    arr = ["", "a", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 4.46μs (-0.11%)

def test_sorter_edge_unicode_strings():
    # Unicode string sorting
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 5.62μs (-0.03%)

def test_sorter_edge_single_characters():
    # List of single characters
    arr = ['z', 'a', 'm', 'b']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 4.88μs (-0.08%)

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

def test_sorter_edge_nested_lists_raises():
    # List with nested lists should raise TypeError
    arr = [1, [2, 3], 4]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_none_in_list():
    # List with None and numbers should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_mutation():
    # The function should mutate the input list (in-place)
    arr = [2, 1]
    sorter(arr)

# --- Large Scale Test Cases ---

def test_sorter_large_sorted():
    # Large already sorted list (performance and correctness)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.4ms -> 51.6μs (354.79%)

def test_sorter_large_reverse():
    # Large reverse sorted list (performance and correctness)
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.0ms -> 19.7ms (0.57%)

def test_sorter_large_random():
    # Large random list (performance and correctness)
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.7ms -> 17.5ms (0.64%)

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 # 24.7ms -> 13.9ms (0.77%)

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 # 29.5ms -> 17.4ms (0.69%)

def test_sorter_large_negative_numbers():
    # Large list with negative numbers
    arr = [random.randint(-10000, 0) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.3ms -> 15.6ms (0.75%)

def test_sorter_large_mixed_floats():
    # Large list with mixed floats and ints
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.1ms -> 16.0ms (0.70%)
# 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-mc13udav and push.