⚡️ Speed up function sorter by 83%

[codeflash-ai]

📄 83% (0.83x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.81 seconds → 2.09 seconds (best of 5 runs)

⚡️ This change will improve the performance of the following benchmarks:

Benchmark File :: Function	Original Runtime	Expected New Runtime	Speedup
code_to_optimize.tests.pytest.benchmarks.test_benchmark_bubble_sort::test_sort	6.83 milliseconds	59.0 microseconds	11470.03%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_compute_and_sort	17.1 milliseconds	10.2 milliseconds	67.88%
code_to_optimize.tests.pytest.benchmarks.test_process_and_sort::test_no_func	6.99 milliseconds	55.6 microseconds	12479.22%

📝 Explanation and details

Here’s a much faster rewritten version of your program. Your original implementation is a classic bubble sort (O(n²)) with redundant passes and repeated calls to len(arr). The program can be made significantly faster by.

• Using Python's built-in sort(), which uses Timsort (O(n log n)).
• Alternatively, if you wish to keep the explicit algorithm, using an optimized Bubble Sort that stops when the list is already sorted, and reduces the inner loop length by each completed pass.
• Reducing repeated attribute lookups (e.g., store len(arr) once).
• Removing unnecessary data swaps (temp) by using Python tuple assignment.

The fastest you can get is with the built-in sort, but if you must use your own loop (since you want the print statements to output the same way and avoid return value changes), here are both options.

---

Option 1: Fastest Built-in Sort

---

Option 2: Optimized Bubble Sort (if custom algorithm is required)

---

Explanation of the optimization:

• arr.sort() leverages Python's Timsort, which is much faster and memory-efficient for almost all practical purposes.
• For the manual method, reducing passes and skipping redundant comparisons makes it much faster (O(n²) worst, O(n) best).
• Early exit if no swaps (already sorted).
• Reduced function overhead and Python list indexing looks.

Both options retain exactly the print outputs and function return of the original. Use Option 1 unless your requirements specifically forbid sort().

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 53 Passed
⏪ Replay Tests	✅ 2 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
benchmarks/codeflash_replay_tests_qru9yosd/test_code_to_optimize_tests_pytest_benchmarks_test_benchmark_bubble_sort__replay_test_0.py::test_code_to_optimize_bubble_sort_sorter	4.53ms	33.6μs	✅13400%
benchmarks/codeflash_replay_tests_qru9yosd/test_code_to_optimize_tests_pytest_benchmarks_test_process_and_sort__replay_test_0.py::test_code_to_optimize_bubble_sort_sorter	4.47ms	34.3μs	✅12915%
benchmarks/test_benchmark_bubble_sort.py::test_sort2	7.76ms	5.02ms	✅54.6%
test_bubble_sort.py::test_sort	955ms	630ms	✅51.6%
test_bubble_sort_conditional.py::test_sort	10.8μs	11.8μs	⚠️-8.47%
test_bubble_sort_import.py::test_sort	964ms	630ms	✅53.0%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	945ms	630ms	✅50.0%
test_bubble_sort_parametrized.py::test_sort_parametrized	578ms	266μs	✅217043%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	140μs	63.8μs	✅120%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for testing string sorting

# 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_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.0μs -> 11.5μs (4.73% faster)

def test_sorter_reverse_sorted_list():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 11.8μs (2.82% faster)

def test_sorter_unsorted_list():
    # Unsorted list should be sorted in ascending order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 10.9μs (4.21% faster)

def test_sorter_list_with_duplicates():
    # List with duplicate values should be sorted with duplicates preserved
    arr = [4, 2, 5, 2, 3, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.3μs -> 11.9μs (3.14% faster)

def test_sorter_negative_numbers():
    # List with negative numbers should be sorted correctly
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 11.2μs (1.49% faster)

def test_sorter_single_element():
    # Single-element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.75μs -> 10.3μs (15.3% slower)

def test_sorter_two_elements():
    # Two-element list, both sorted and unsorted
    arr1 = [2, 1]
    arr2 = [1, 2]
    codeflash_output = sorter(arr1.copy()) # 10.3μs -> 10.2μs (0.813% faster)
    codeflash_output = sorter(arr2.copy()) # 8.29μs -> 8.33μs (0.492% slower)

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

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 9.67μs (5.61% faster)

def test_sorter_all_equal_elements():
    # All elements equal should remain unchanged
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 10.2μs (7.78% faster)

def test_sorter_large_negative_and_positive():
    # List with large negative and positive values
    arr = [-1000000, 999999, 0, 100, -100, 500, -500]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.4μs -> 12.8μs (4.56% faster)

def test_sorter_floats():
    # List with floating point numbers
    arr = [3.1, 2.2, 5.5, 4.4, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.8μs -> 13.5μs (2.79% faster)

def test_sorter_mixed_int_float():
    # List with both ints and floats
    arr = [1, 2.2, 3, 0.5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.8μs -> 13.3μs (3.45% slower)

def test_sorter_strings():
    # List of strings should be sorted lexicographically
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.6μs -> 11.3μs (2.58% faster)

def test_sorter_strings_case_sensitivity():
    # List of strings with mixed case
    arr = ["Banana", "apple", "Cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 11.1μs (1.88% slower)

def test_sorter_empty_strings():
    # List with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 11.8μs (2.47% slower)

def test_sorter_unicode_strings():
    # List with unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 11.5μs (4.69% faster)

def test_sorter_mutable_input():
    # Ensure the function sorts in-place (since it modifies the input)
    arr = [3, 2, 1]
    sorter(arr)

def test_sorter_list_with_bool():
    # List with booleans and ints (True==1, False==0)
    arr = [True, False, 1, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 11.4μs (0.360% slower)

def test_sorter_list_with_large_and_small_floats():
    # List with very large and very small floats
    arr = [1e-10, 1e10, -1e10, 0.0, -1e-10]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.8μs -> 14.4μs (3.77% slower)

def test_sorter_list_with_nan_inf():
    # List with NaN and infinity. NaN is always sorted last in Python's sort
    arr = [float('inf'), float('-inf'), float('nan'), 1, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.9μs -> 12.4μs (4.03% slower)

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

def test_sorter_heterogeneous_types():
    # List with incompatible types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

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

def test_sorter_large_random_list():
    # Large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.4ms -> 18.3ms (65.6% 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 # 28.1ms -> 15.6ms (80.4% faster)

def test_sorter_large_sorted():
    # Already sorted large list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.4ms -> 56.9μs (35702% 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 # 33.9ms -> 22.9ms (47.8% 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 # 32.7ms -> 19.2ms (70.2% 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 # 29.9ms -> 17.4ms (71.4% 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 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

# 1. Basic Test Cases

def test_sorter_sorted_input():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.8μs (0.380% slower)

def test_sorter_reverse_sorted_input():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 11.0μs (0.752% faster)

def test_sorter_unsorted_input():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 9.42μs (16.8% faster)

def test_sorter_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 9.88μs (10.1% faster)

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-1, -3, 2, 0, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 10.8μs (2.70% faster)

def test_sorter_floats_and_integers():
    # List with floats and integers
    arr = [3.2, 1, 2.5, 0, -1.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 14.0μs -> 13.2μs (5.66% faster)

def test_sorter_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.54μs -> 10.0μs (14.9% slower)

def test_sorter_two_elements():
    # List with two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.00μs -> 9.71μs (7.29% slower)

def test_sorter_two_elements_sorted():
    # List with two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 9.92μs (1.68% faster)

def test_sorter_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 10.8μs (3.09% faster)

def test_sorter_mixed_case_strings():
    # List of strings with mixed case (lex sort)
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 10.6μs (0.784% faster)

# 2. Edge Test Cases

def test_sorter_empty_list():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.62μs -> 9.75μs (1.28% slower)

def test_sorter_all_identical_elements():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 10.3μs (0.816% faster)

def test_sorter_large_negative_and_positive():
    # List with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.5μs -> 12.7μs (1.32% slower)

def test_sorter_already_sorted_with_duplicates():
    # Sorted list with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 10.6μs (4.31% faster)


def test_sorter_mixed_types_fails():
    # List with mixed types (int and str) should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_nan_and_inf():
    # List with float('nan'), float('inf'), float('-inf')
    arr = [float('nan'), float('inf'), float('-inf'), 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.7μs -> 12.3μs (3.06% faster)

def test_sorter_unicode_strings():
    # Unicode string sorting
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.4μs -> 11.6μs (6.84% faster)

# 3. Large Scale Test Cases

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-100000, -99000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.6ms -> 18.7ms (64.0% faster)

def test_sorter_large_sorted_input():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.7ms -> 56.5μs (36437% faster)

def test_sorter_large_reverse_sorted_input():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.0ms -> 23.2ms (46.8% 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 # 27.6ms -> 15.6ms (76.6% faster)

def test_sorter_large_strings():
    # Large list of random strings
    arr = [
        ''.join(random.choices(string.ascii_letters, k=10))
        for _ in range(1000)
    ]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.6ms -> 19.5ms (66.9% faster)

def test_sorter_large_floats():
    # 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 # 28.8ms -> 17.3ms (66.3% 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-mcfhjz2x and push.