⚡️ Speed up function sorter by 45,852%

[codeflash-ai]

📄 45,852% (458.52x) speedup for sorter in codeflash/galileo/bubble_sort.py

⏱️ Runtime : 797 milliseconds → 1.73 milliseconds (best of 575 runs)

📝 Explanation and details

Here is a highly optimized version of your sorter function. The original code uses bubble sort, which is O(n²) and very slow for large arrays. Python’s built-in sort() (Timsort) is O(n log n) and highly optimized in C.

You requested that the function signature and return value remain the same, and existing comments are preserved unless a relevant portion of the code is changed.

This rewrites the sorting operation to be dramatically faster for all but the smallest lists.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 3 Passed
🌀 Generated Regression Tests	✅ 60 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
test_bubble_sort.py::test_sort	568ms	132μs	✅429688%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for min/max int tests

# imports
import pytest  # used for our unit tests
from galileo.bubble_sort import sorter

# unit tests

# -------------------------------
# 1. Basic Test Cases
# -------------------------------

def test_sorter_sorted_list():
    # Already sorted input should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 3.00μs (41.7% faster)

def test_sorter_reverse_sorted_list():
    # Reverse sorted input should be sorted in ascending order
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.00μs (65.3% faster)

def test_sorter_unsorted_list():
    # Unsorted input should be sorted
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.54μs -> 3.04μs (49.3% faster)

def test_sorter_with_duplicates():
    # Input with duplicates should handle them correctly
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.54μs -> 3.08μs (47.3% faster)

def test_sorter_all_equal():
    # All elements the same should remain unchanged
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.79μs -> 2.88μs (31.9% faster)

def test_sorter_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.25μs -> 3.08μs (5.42% faster)

def test_sorter_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 2.88μs (34.8% faster)

def test_sorter_negative_numbers():
    # Negative numbers should be sorted correctly
    arr = [-3, -1, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.12μs -> 3.08μs (33.8% faster)

def test_sorter_mixed_positive_negative():
    # Mixed positive and negative numbers
    arr = [0, -1, 2, -3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.00μs (55.5% faster)

def test_sorter_floats_and_ints():
    # Mixture of floats and ints
    arr = [3.2, 1, 2.5, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 3.58μs (62.8% faster)

def test_sorter_strings():
    # Sorting strings lexicographically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.08μs -> 3.00μs (36.1% faster)

def test_sorter_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.33μs -> 3.04μs (9.60% faster)

def test_sorter_empty_list():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.42μs -> 2.83μs (20.6% faster)

# -------------------------------
# 2. Edge Test Cases
# -------------------------------

def test_sorter_large_and_small_numbers():
    # Test with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 3.67μs (46.6% faster)

def test_sorter_floats_precision():
    # Test with floats that are very close together
    arr = [1.000001, 1.0000001, 1.00000001, 1.00001]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 3.96μs (37.9% faster)

def test_sorter_unicode_strings():
    # Unicode strings should sort lexicographically
    arr = ["ápple", "apple", "äpple", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 3.42μs (47.6% faster)

def test_sorter_case_sensitive_strings():
    # Sorting is case-sensitive by default in Python
    arr = ["banana", "Apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.17μs -> 2.92μs (42.9% faster)

def test_sorter_empty_strings():
    # Empty strings should sort before non-empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.33μs (35.0% faster)

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

def test_sorter_mixed_types_raises_typeerror():
    # Mixing int and string should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_nested_lists_raises_typeerror():
    # Nested lists cannot be sorted with numbers
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_large_negative_numbers():
    # Large negative numbers with small positives
    arr = [-999999999, 1, 0, -1000000000]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.54μs -> 3.08μs (47.3% faster)

def test_sorter_long_strings():
    # Sorting long strings
    arr = ["a"*100, "b"*99, "a"*101]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.75μs (38.9% faster)

# -------------------------------
# 3. Large Scale Test Cases
# -------------------------------

def test_sorter_large_random_ints():
    # Sorting a large list of random ints
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 16.5ms -> 59.2μs (27826% faster)

def test_sorter_large_random_floats():
    # 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 # 16.0ms -> 268μs (5878% faster)

def test_sorter_large_sorted_input():
    # Sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.9μs -> 28.0μs (85.4% faster)

def test_sorter_large_reverse_sorted_input():
    # Sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.0ms -> 28.0μs (71093% 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 # 14.2ms -> 45.9μs (30937% faster)

def test_sorter_large_strings():
    # Sorting a 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 # 17.7ms -> 96.2μs (18326% faster)

def test_sorter_large_all_equal():
    # Large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 50.6μs -> 26.9μs (88.1% faster)

def test_sorter_large_mixed_signs():
    # Large list with positive and negative numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 16.4ms -> 58.2μs (28123% 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 value tests

# imports
import pytest  # used for our unit tests
from galileo.bubble_sort import sorter

# unit tests

# 1. Basic Test Cases

def test_empty_list():
    # Sorting an empty list should return an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 2.92μs (50.0% faster)

def test_single_element():
    # Sorting a single-element list should return the same single-element list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 2.88μs (37.7% faster)

def test_already_sorted():
    # Sorting an already sorted list should not change it
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 2.96μs (33.8% faster)

def test_reverse_sorted():
    # Sorting a reverse-sorted list should return a sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 3.00μs (55.5% faster)

def test_unsorted_with_duplicates():
    # Sorting a list with duplicates should place duplicates together
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.08μs (44.6% faster)

def test_negative_numbers():
    # Sorting a list with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.04μs -> 3.17μs (27.7% faster)

def test_mixed_positive_and_negative():
    # Sorting a list with both positive and negative numbers
    arr = [4, -1, 0, -3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.54μs -> 3.04μs (49.3% faster)

def test_all_duplicates():
    # Sorting a list where all elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.67μs -> 3.04μs (20.5% faster)

def test_floats():
    # Sorting a list with floats and integers
    arr = [3.1, 2.2, 5.5, 1.0, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 3.62μs (60.9% faster)

def test_strings():
    # Sorting a list of strings alphabetically
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.25μs -> 2.96μs (43.7% faster)

def test_mixed_case_strings():
    # Sorting a list of strings with different cases (should be case-sensitive)
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.79μs -> 3.08μs (23.0% faster)

# 2. Edge Test Cases

def test_large_negative_and_positive():
    # Sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 1, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.33μs (45.0% faster)

def test_list_with_none():
    # Sorting a list with None should raise a TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_list_with_unorderable_types():
    # Sorting a list with incomparable types should raise a TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_list_of_lists():
    # Sorting a list of lists should sort by lexicographical order
    arr = [[2, 3], [1, 2], [1, 1]]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.33μs (53.8% faster)

def test_list_of_empty_lists():
    # Sorting a list of empty lists and non-empty lists
    arr = [[], [1], [], [0]]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.71μs -> 3.17μs (48.7% faster)

def test_list_with_boolean_values():
    # Sorting a list with booleans (should treat False < True < numbers)
    arr = [True, False, 1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.12μs (52.0% faster)

def test_list_with_nan():
    # Sorting a list with float('nan') - nan is not orderable, so it should appear at the end
    arr = [3, float('nan'), 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.42μs -> 3.08μs (43.2% faster)

def test_list_with_infinity():
    # Sorting a list with float('inf') and float('-inf')
    arr = [3, float('inf'), 2, float('-inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.17μs -> 3.29μs (57.0% faster)

def test_list_with_unicode_strings():
    # Sorting a list with unicode strings
    arr = ["ápple", "apple", "äpple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.33μs (52.5% faster)

def test_list_with_long_strings():
    # Sorting a list with very long strings
    arr = ["a"*100, "b"*99, "a"*101]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.54μs -> 3.83μs (44.6% faster)

# 3. Large Scale Test Cases

def test_large_sorted_list():
    # Sorting a large already sorted list should be fast (best case for bubble sort)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.7μs -> 28.5μs (81.7% faster)

def test_large_reverse_list():
    # Sorting a large reverse-sorted list should return a sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.8ms -> 27.9μs (70812% faster)

def test_large_random_list():
    # Sorting a large list of random integers
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 17.5ms -> 53.9μs (32420% faster)

def test_large_list_with_duplicates():
    # 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 # 14.2ms -> 45.6μs (31095% faster)

def test_large_list_of_strings():
    # Sorting a large list of random lowercase 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 # 18.0ms -> 87.3μs (20462% faster)

def test_large_list_of_floats():
    # Sorting a large list of random floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 15.9ms -> 261μs (5964% faster)

def test_large_list_of_lists():
    # Sorting a large list of lists by lexicographical order
    arr = [random.sample(range(1000), 3) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter([x.copy() for x in arr]); result = codeflash_output # 24.4ms -> 271μs (8899% faster)

def test_large_list_with_edge_integers():
    # Sorting a large list including sys.maxsize and -sys.maxsize-1
    arr = [random.randint(-1000, 1000) for _ in range(998)] + [sys.maxsize, -sys.maxsize-1]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 17.0ms -> 82.0μs (20656% 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-mcmr6m3r and push.