⚡️ Speed up function sorter by 56%

[codeflash-ai]

📄 56% (0.56x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.73 seconds → 2.39 seconds (best of 5 runs)

📝 Explanation and details

Here's an optimized version of your program.
Your code uses bubble sort with unnecessary recomputation of len(arr). Instead, use Python’s built-in sort (which is in-place and much, much faster).
If you want to preserve the manual sort for educational purposes, at least minimize attribute and function calls by keeping the length calculation outside of the loop, and exit early if no swaps were performed (classic bubble sort optimization).

But, for runtime, the built-in sort is best. Since your function returns the sorted array in-place, this is a drop-in replacement.

Fastest version (use built-in sort):

---

If you want to keep a manual implementation with loop optimizations and swap-detection:

This avoids unnecessary passes and decreases the inner loop range as elements bubble up.

---

Summary:

• Use arr.sort() for best speed in production.
• If you must show sorting logic, use the manually optimized Bubble Sort shown above.

Both return the array as before and print the same messages.

✅ 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 and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
benchmarks/test_benchmark_bubble_sort.py::test_sort2	7.74ms	5.05ms	✅53.3%
test_bubble_sort.py::test_sort	906ms	627ms	✅44.4%
test_bubble_sort_conditional.py::test_sort	13.0μs	12.3μs	✅5.76%
test_bubble_sort_import.py::test_sort	914ms	629ms	✅45.3%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	928ms	625ms	✅48.4%
test_bubble_sort_parametrized.py::test_sort_parametrized	572ms	267ms	✅114%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	136μs	102μs	✅32.9%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for sys.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_empty_list():
    # Sorting an empty list should return an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.75μs -> 9.75μs (0.000% faster)

def test_sorter_single_element():
    # Sorting a single-element list should return the same list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.79μs -> 9.92μs (1.26% slower)

def test_sorter_sorted_list():
    # Sorting an already sorted list should not change it
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 10.7μs (1.56% slower)

def test_sorter_reverse_sorted_list():
    # Sorting a reverse-sorted list should return it in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 10.8μs (2.69% faster)

def test_sorter_unsorted_list():
    # Sorting a randomly unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 10.6μs (0.794% faster)

def test_sorter_with_duplicates():
    # Sorting a list with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 10.3μs (0.406% faster)

def test_sorter_negative_numbers():
    # Sorting a list with negative numbers
    arr = [-3, -1, -2, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 10.4μs (0.405% slower)

def test_sorter_floats():
    # Sorting a list with floating point numbers
    arr = [3.1, 2.2, 5.5, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.5μs -> 12.3μs (1.37% faster)

def test_sorter_mixed_integers_and_floats():
    # Sorting a list with both integers and floats
    arr = [1, 2.2, 3, 1.1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 11.8μs (2.46% faster)

def test_sorter_strings():
    # Sorting a list of strings (lexicographical order)
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 10.4μs (1.60% faster)

def test_sorter_single_type_bools():
    # Sorting a list of booleans (False < True)
    arr = [True, False, True]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.38μs -> 10.1μs (7.02% slower)

# ----------------
# EDGE TEST CASES
# ----------------

def test_sorter_all_identical_elements():
    # Sorting a list where all elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 10.0μs (1.25% faster)

def test_sorter_large_and_small_numbers():
    # Sorting a list with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999, -999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 11.8μs (5.63% slower)
    expected = [-sys.maxsize-1, -999999, 0, 999999, sys.maxsize]

def test_sorter_minimal_difference():
    # Sorting a list where numbers differ by minimal float difference
    arr = [1.0000001, 1.0, 1.0000002]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.9μs (1.15% slower)

def test_sorter_unicode_strings():
    # Sorting a list of unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 11.7μs (3.92% slower)

def test_sorter_empty_strings():
    # Sorting a list with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 10.4μs (5.21% faster)

def test_sorter_bools_and_ints():
    # Sorting a list with booleans and integers (bool is subclass of int: False==0, True==1)
    arr = [True, False, 1, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 10.8μs (1.15% faster)

def test_sorter_already_sorted_with_duplicates():
    # Already sorted list with duplicates
    arr = [1, 2, 2, 3, 3, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 10.2μs (2.04% faster)

def test_sorter_reverse_sorted_with_duplicates():
    # Reverse sorted list with duplicates
    arr = [4, 3, 3, 2, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 10.8μs (2.69% faster)

def test_sorter_large_negative_and_positive_floats():
    # Large negative and positive floats
    arr = [-1e308, 1e308, 0.0, 1e-308, -1e-308]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 15.5μs -> 13.9μs (11.7% faster)

def test_sorter_mutation_of_input():
    # Ensure the function returns the sorted list and mutates the input in-place
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr); result = codeflash_output # 9.38μs -> 8.58μs (9.23% faster)

# ----------------------
# LARGE SCALE TEST CASES
# ----------------------

def test_sorter_large_random_integers():
    # Sorting a large list (1000 elements) of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.0ms -> 18.7ms (61.0% faster)

def test_sorter_large_sorted_input():
    # Sorting a large already sorted list (1000 elements)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.2ms -> 9.34ms (116% faster)

def test_sorter_large_reverse_sorted_input():
    # Sorting a large reverse sorted list (1000 elements)
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.9ms -> 22.8ms (44.5% faster)

def test_sorter_large_identical_elements():
    # Sorting a large list of identical elements (1000 elements)
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.5ms -> 9.09ms (125% faster)

def test_sorter_large_strings():
    # Sorting a large list of random lowercase strings (length 3, 1000 elements)
    arr = [''.join(random.choices(string.ascii_lowercase, k=3)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 35.8ms -> 20.3ms (76.8% faster)

def test_sorter_large_floats():
    # Sorting a large list of random floats (1000 elements)
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.0ms -> 17.3ms (67.7% 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 test cases
import string  # used for string sorting tests
import sys  # for testing with sys.maxsize

# 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 # 9.42μs -> 10.5μs (9.96% slower)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 10.4μs (2.01% faster)

def test_sorter_basic_unsorted():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 10.3μs (6.48% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.4μs (3.61% faster)

def test_sorter_basic_all_equal():
    # All elements equal
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.12μs -> 9.83μs (7.21% slower)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-1, -3, 2, 0, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.6μs (1.96% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.62μs -> 9.67μs (0.434% slower)

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [5, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.58μs -> 9.67μs (0.859% slower)

def test_sorter_basic_two_elements_sorted():
    # Two element list, already sorted
    arr = [3, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 9.50μs (2.19% faster)

# EDGE TEST CASES

def test_sorter_edge_empty():
    # Empty list should return an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.54μs -> 8.21μs (16.2% faster)

def test_sorter_edge_large_integers():
    # 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 # 12.0μs -> 11.6μs (3.58% faster)

def test_sorter_edge_floats():
    # List with floats and integers
    arr = [3.2, 1, 2.5, 0, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.6μs -> 12.3μs (10.1% faster)

def test_sorter_edge_negative_zero():
    # List with -0.0 and 0.0 (should treat as equal)
    arr = [0.0, -0.0, 1, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 10.6μs (5.88% faster)

def test_sorter_edge_strings():
    # List of strings (should sort lexicographically)
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 10.2μs (2.84% faster)

def test_sorter_edge_mixed_types():
    # List with mixed types should raise TypeError in Python 3
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 44.1μs -> 43.6μs (1.15% faster)

def test_sorter_edge_unicode_strings():
    # Unicode strings should sort according to Unicode code points
    arr = ["á", "a", "ä"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 10.8μs (6.92% slower)

def test_sorter_edge_custom_objects():
    # Sorting custom objects without __lt__ should raise TypeError
    class Foo:
        pass
    arr = [Foo(), Foo()]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.2μs -> 44.0μs (1.70% slower)

def test_sorter_edge_nan_values():
    # List with float('nan') values
    arr = [float('nan'), 1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 10.2μs (1.65% faster)
    # nan is not equal to anything, but in sort, it can end up anywhere
    # Check that 1 and 2 are in order, and nan is present
    filtered = [x for x in result if x != float('nan')]

def test_sorter_edge_inf_values():
    # List with inf and -inf
    arr = [float('inf'), -1, 0, float('-inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 10.9μs (5.72% faster)

# 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 # 20.7ms -> 9.20ms (125% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.6ms -> 22.9ms (47.1% faster)

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 # 31.3ms -> 19.6ms (59.6% faster)

def test_sorter_large_all_equal():
    # Large list with all elements equal
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.8ms -> 9.03ms (120% 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 # 27.9ms -> 15.8ms (76.6% faster)

def test_sorter_large_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 # 33.4ms -> 19.8ms (68.5% faster)

def test_sorter_large_negative_numbers():
    # Large list with negative numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.2ms -> 18.1ms (66.9% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.0ms -> 17.4ms (66.9% 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-md5fbyut and push.