⚡️ Speed up function sorter by 104,077%

[codeflash-ai]

📄 104,077% (1,040.77x) speedup for sorter in codeflash/bubble_sort.py

⏱️ Runtime : 2.09 seconds → 2.01 milliseconds (best of 123 runs)

📝 Explanation and details

The optimization replaces a manual bubble sort implementation with Python's built-in arr.sort() method, delivering a massive 104,077% speedup.

What changed:

• Removed the nested loops that implement bubble sort (O(n²) complexity)
• Replaced with a single call to arr.sort() which uses Timsort (O(n log n) complexity)

Why this is dramatically faster:
The original bubble sort performs ~62 million comparisons and ~27 million swaps for larger datasets, as shown in the line profiler. Each comparison (arr[j] > arr[j + 1]) and swap operation takes significant time when repeated millions of times. Python's built-in sort() uses Timsort, a highly optimized hybrid stable sorting algorithm that:

• Has O(n log n) time complexity vs O(n²) for bubble sort
• Is implemented in C, making individual operations much faster
• Uses adaptive techniques that perform better on partially sorted data

Performance gains by test case type:

• Small lists (≤10 elements): 15-40% faster - modest gains since overhead dominates
• Medium lists (~100 elements): 1,000-2,000% faster - algorithm efficiency starts showing
• Large lists (1000+ elements): 10,000-90,000% faster - massive gains where O(n²) vs O(n log n) difference is most pronounced
• Best case scenarios: Already sorted or reverse sorted large lists show the highest speedups (55,000-92,000%) due to Timsort's adaptive nature

The optimization maintains identical behavior including in-place sorting and all print statements.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 3 Passed
🌀 Generated Regression Tests	✅ 55 Passed
⏪ Replay Tests	✅ 3 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_bubble_sort.py::test_sort	883ms	155μs	✅566541%
test_pytest_teststest_bubble_sort_py__replay_test_0.py::test_codeflash_bubble_sort_sorter	879ms	157μs	✅557656%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for maxsize edge case

# imports
import pytest  # used for our unit tests
from codeflash.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 # 11.0μs -> 8.75μs (26.2% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.38μs (31.3% faster)

def test_sorter_basic_random():
    # Random order list should be sorted
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.33μs (26.0% faster)

def test_sorter_basic_duplicates():
    # List with duplicates should be sorted with duplicates adjacent
    arr = [4, 2, 2, 3, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.33μs -> 7.96μs (17.3% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers should be sorted correctly
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.33μs (29.0% faster)

def test_sorter_basic_single_element():
    # Single-element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.54μs -> 8.04μs (18.7% faster)

def test_sorter_basic_two_elements():
    # Two-element list should be sorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.50μs -> 8.08μs (17.5% faster)

def test_sorter_basic_strings():
    # List of strings should be sorted lexicographically
    arr = ['banana', 'apple', 'cherry']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.42μs (24.2% faster)

def test_sorter_basic_floats():
    # List of floats should be sorted numerically
    arr = [1.1, 3.3, 2.2, 0.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.9μs -> 8.71μs (36.4% faster)

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

def test_sorter_empty_list():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.21μs -> 7.96μs (15.7% faster)

def test_sorter_all_equal():
    # List where all elements are equal should remain unchanged
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 8.17μs (23.5% faster)

def test_sorter_min_max_integers():
    # List with min and max integer values
    arr = [sys.maxsize, -sys.maxsize-1, 0, 1, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.7μs -> 8.83μs (32.5% faster)

def test_sorter_large_negative_positive():
    # List with large negative and positive numbers
    arr = [-999999999, 999999999, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.17μs (25.0% faster)

def test_sorter_already_sorted_large():
    # Large already sorted list
    arr = list(range(100))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 151μs -> 11.9μs (1174% faster)

def test_sorter_reverse_sorted_large():
    # Large reverse sorted list
    arr = list(range(99, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 261μs -> 11.0μs (2281% faster)

def test_sorter_strings_case_sensitive():
    # Strings with different casing should be sorted by ASCII order
    arr = ['apple', 'Banana', 'banana', 'Apple']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 8.54μs (29.3% faster)

def test_sorter_unicode_strings():
    # Unicode strings should be sorted by Unicode code point order
    arr = ['á', 'a', 'ä', 'â']
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 8.92μs (24.8% faster)

def test_sorter_floats_and_integers():
    # List with both floats and integers should be sorted numerically
    arr = [1, 2.2, 3, 0.5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.8μs -> 9.92μs (29.0% faster)

def test_sorter_mutation():
    # Ensure the function sorts in-place and returns the same object
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 10.1μs -> 8.62μs (17.4% faster)

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

def test_sorter_large_random_integers():
    # Sorting a large random list of integers
    arr = random.sample(range(-1000, 0), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.4ms -> 66.7μs (45519% faster)

def test_sorter_large_random_floats():
    # Sorting a large random list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.6ms -> 286μs (9905% faster)

def test_sorter_large_random_strings():
    # Sorting a large random list of 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 # 31.2ms -> 111μs (27872% 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 # 26.4ms -> 57.5μs (45924% faster)

def test_sorter_large_alternating():
    # Large list alternating between two values
    arr = [1, 0] * 500
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 23.2ms -> 50.0μs (46313% faster)

# ---------------------------
# Robustness Test Cases
# ---------------------------

def test_sorter_type_error_on_mixed_types():
    # Should raise TypeError when sorting list with incomparable types
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.3μs -> 42.4μs (2.26% faster)

def test_sorter_type_error_on_unorderable():
    # Should raise TypeError when sorting list with unorderable types
    arr = [object(), object()]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.7μs -> 42.2μs (3.35% faster)

def test_sorter_none_in_list():
    # Should raise TypeError when None is present with other types
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 42.6μs -> 41.5μs (2.61% faster)


#------------------------------------------------
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 codeflash.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 # 10.0μs -> 9.17μs (9.56% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.04μs (34.2% faster)

def test_sorter_basic_unsorted():
    # Unsorted list should become sorted
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 8.04μs (28.5% faster)

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.17μs (28.1% faster)

def test_sorter_basic_negatives():
    # List with negative numbers
    arr = [-1, -3, 2, 0, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.12μs (29.2% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.38μs -> 8.21μs (14.2% faster)

def test_sorter_basic_two_elements():
    # List with two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 7.79μs (24.6% faster)

def test_sorter_basic_all_equal():
    # List where all elements are the same
    arr = [5, 5, 5, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.58μs -> 7.92μs (21.1% faster)

def test_sorter_basic_floats():
    # List with floating point numbers
    arr = [2.5, 1.1, 3.3, 2.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 8.54μs (38.6% faster)

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 8.83μs (15.6% faster)

def test_sorter_basic_mixed_case_strings():
    # List of strings with mixed cases
    arr = ["banana", "Apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.79μs -> 8.17μs (19.9% faster)

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

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.17μs -> 7.92μs (15.8% faster)

def test_sorter_edge_large_numbers():
    # List with very large integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.9μs -> 9.08μs (31.2% faster)

def test_sorter_edge_large_negative_floats():
    # Large negative and positive floats
    arr = [-1e308, 1e308, 0.0, 1e-308, -1e-308]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 15.0μs -> 11.5μs (30.8% faster)

def test_sorter_edge_already_sorted_with_duplicates():
    # Already sorted list with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.12μs (31.8% faster)

def test_sorter_edge_all_negative():
    # All negative numbers
    arr = [-5, -10, -3, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 8.21μs (26.9% faster)

def test_sorter_edge_all_zeros():
    # All zeros
    arr = [0, 0, 0, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.67μs -> 8.04μs (20.2% faster)

def test_sorter_edge_strings_with_empty_and_spaces():
    # Strings with empty string and spaces
    arr = ["", " ", "apple", "  ", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 8.29μs (35.2% faster)

def test_sorter_edge_unicode_strings():
    # Unicode strings
    arr = ["éclair", "apple", "Éclair", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 8.46μs (21.7% faster)

def test_sorter_edge_mutation():
    # Ensure the function mutates the list in place (as per implementation)
    arr = [3, 1, 2]
    sorter(arr) # 10.0μs -> 8.04μs (24.4% faster)

def test_sorter_edge_type_error():
    # List with incompatible types should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 45.5μs -> 41.2μs (10.6% faster)

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

def test_sorter_large_random_integers():
    # Large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)  # 1000 unique integers
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.8ms -> 69.1μs (42982% faster)

def test_sorter_large_many_duplicates():
    # Large list with many duplicate values
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 26.6ms -> 58.0μs (45753% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.8ms -> 35.3μs (55985% 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 # 33.0ms -> 35.8μs (92003% 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 # 31.2ms -> 101μs (30712% 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 # 28.7ms -> 286μs (9900% faster)

def test_sorter_large_already_all_equal():
    # Large list where all elements are the same
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.2ms -> 32.9μs (58209% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

⏪ Replay Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_bubble_sort.py::test_sort	883ms	155μs	✅566541%
test_pytest_teststest_bubble_sort_py__replay_test_0.py::test_codeflash_bubble_sort_sorter	879ms	157μs	✅557656%

To edit these changes git checkout codeflash/optimize-sorter-mdtab4a2 and push.