⚡️ Speed up function sorter by 177,794%

[codeflash-ai]

📄 177,794% (1,777.94x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.41 seconds → 1.92 milliseconds (best of 433 runs)

📝 Explanation and details

The optimized code replaces a manual bubble sort implementation (O(n²)) with Python's built-in arr.sort() method, which uses Timsort - a highly optimized hybrid sorting algorithm with O(n log n) complexity.

Key changes:

• Eliminated nested loops: The original bubble sort required ~115M iterations for large inputs, now replaced with a single arr.sort() call
• Algorithm complexity improvement: From O(n²) bubble sort to O(n log n) Timsort
• Leveraged native implementation: Python's sort is implemented in C and heavily optimized

Why this is faster:

• Algorithmic advantage: For 1000 elements, bubble sort needs ~1M comparisons vs ~10K for Timsort
• Native C implementation: Built-in sort runs at C speed rather than interpreted Python
• Adaptive optimizations: Timsort performs even better on partially sorted data

Performance characteristics from tests:

• Small arrays (≤10 elements): 37-69% faster due to reduced overhead
• Large arrays (1000 elements): 9,000x-106,000x faster due to algorithmic superiority
• Best case scenarios: Already sorted lists see 62,000x+ speedup
• Worst case scenarios: Reverse-sorted lists see 106,000x+ speedup

The optimization maintains identical behavior including in-place sorting, error handling for incomparable types, and all output formatting.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 61 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	7.01ms	16.6μs	✅42187%
test_bubble_sort.py::test_sort	838ms	138μs	✅607657%
test_bubble_sort_conditional.py::test_sort	5.58μs	3.12μs	✅78.7%
test_bubble_sort_import.py::test_sort	837ms	137μs	✅609992%
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	841ms	138μs	✅608833%
test_bubble_sort_parametrized.py::test_sort_parametrized	509ms	136μs	✅372698%
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	102μs	21.0μs	✅387%

🌀 Generated Regression Tests and Runtime

import random # used for generating large random test cases import string # used for string sorting tests import sys # used for edge cases with large/small numbers # 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 arr = [1, 2, 3, 4, 5] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.08μs (59.5% faster) def test_sorter_basic_unsorted(): # Simple unsorted list arr = [5, 3, 1, 4, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.96μs -> 3.08μs (60.8% faster) def test_sorter_basic_reverse(): # Reverse sorted list arr = [5, 4, 3, 2, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.62μs -> 3.00μs (54.2% faster) def test_sorter_basic_duplicates(): # List with duplicates arr = [2, 3, 1, 2, 3, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.08μs (69.0% faster) def test_sorter_basic_negative_numbers(): # List with negative numbers arr = [-1, -3, -2, 0, 2, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.12μs -> 3.29μs (55.7% faster) def test_sorter_basic_mixed_numbers(): # List with positive, negative, and zero arr = [0, -10, 5, 3, -2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.21μs -> 3.12μs (66.7% faster) def test_sorter_basic_all_equal(): # List where all elements are equal arr = [7, 7, 7, 7] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.29μs -> 3.00μs (43.0% faster) def test_sorter_basic_single_element(): # List with a single element arr = [42] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.00μs -> 2.92μs (37.2% faster) def test_sorter_basic_empty(): # Empty list arr = [] codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.50μs -> 2.88μs (21.7% faster) def test_sorter_basic_floats(): # List with floats arr = [3.2, 1.5, 4.8, 2.1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.71μs -> 3.71μs (54.0% faster) def test_sorter_basic_mixed_int_float(): # List with ints and floats arr = [1, 2.2, 3, 0.5] codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 3.58μs (67.5% faster) def test_sorter_basic_strings(): # List of strings arr = ["banana", "apple", "cherry"] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 3.17μs (52.6% faster) def test_sorter_basic_mixed_case_strings(): # List of strings with mixed case arr = ["Banana", "apple", "Cherry"] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.46μs -> 3.25μs (37.2% faster) # --------------------------- # Edge Test Cases # --------------------------- def test_sorter_edge_large_numbers(): # 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 # 6.29μs -> 3.62μs (73.6% faster) def test_sorter_edge_floats_precision(): # List with floats that are very close to each other arr = [1.000001, 1.0000001, 1.00000001, 1.0] codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 3.92μs (54.3% faster) def test_sorter_edge_all_negative(): # All negative numbers, unordered arr = [-5, -1, -3, -2, -4] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.08μs -> 3.12μs (62.7% faster) def test_sorter_edge_strings_special_chars(): # Strings with special characters arr = ["!apple", "#banana", "@cherry", "apple"] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 3.29μs (44.3% faster) def test_sorter_edge_unicode_strings(): # Strings with unicode characters arr = ["café", "apple", "banana", "ápple"] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 3.75μs (50.0% faster) def test_sorter_edge_none_values(): # List containing None should raise TypeError arr = [1, None, 2] with pytest.raises(TypeError): sorter(arr.copy()) # 2.67μs -> 1.83μs (45.4% faster) def test_sorter_edge_incomparable_types(): # List with incomparable types should raise TypeError arr = [1, "a", 2] with pytest.raises(TypeError): sorter(arr.copy()) # 2.42μs -> 1.83μs (31.9% faster) def test_sorter_edge_tuple_elements(): # List with tuples (tuples are comparable if their elements are comparable) arr = [(2, 1), (1, 2), (1, 1)] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 3.75μs (48.9% faster) def test_sorter_edge_mutation(): # Ensure that the function mutates the input list (since it sorts in place) arr = [3, 2, 1] codeflash_output = sorter(arr); result = codeflash_output # 4.71μs -> 3.04μs (54.8% faster) def test_sorter_edge_large_negative_and_positive(): # List with both very large negative and positive numbers arr = [-10**18, 10**18, 0, 1, -1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.71μs -> 3.50μs (63.1% faster) def test_sorter_edge_very_small_floats(): # List with very small floats arr = [1e-10, -1e-10, 0.0, 1e-12] codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.75μs -> 4.38μs (54.3% faster) def test_sorter_edge_integers_and_bools(): # List with integers and booleans (bool is subclass of int) arr = [True, False, 2, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.17μs (53.9% faster) def test_sorter_edge_large_string_length(): # List with very long strings arr = ["a"*100, "a"*99 + "b", "a"*101] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 4.08μs (36.7% faster) # --------------------------- # 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 # 18.5ms -> 29.7μs (62264% faster) def test_sorter_large_reverse(): # Large reverse sorted list arr = list(range(999, -1, -1)) codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.1ms -> 29.3μs (105941% faster) def test_sorter_large_random(): # Large random list of integers arr = random.sample(range(1000), 1000) expected = sorted(arr) codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.2ms -> 61.7μs (47289% 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 # 24.8ms -> 50.7μs (48836% 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 # 30.3ms -> 108μs (27852% 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 # 27.0ms -> 286μs (9337% 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 # 18.1ms -> 28.2μs (64210% faster) def test_sorter_large_alternating(): # Large list alternating between two values arr = [0, 1] * 500 expected = sorted(arr) codeflash_output = sorter(arr.copy()); result = codeflash_output # 21.6ms -> 43.8μs (49353% faster) def test_sorter_large_edge_extremes(): # List with min/max int, min/max float, and zeros arr = [sys.maxsize, -sys.maxsize-1, sys.float_info.max, -sys.float_info.max, 0, 0.0] codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.6μs -> 7.25μs (59.8% faster) expected = sorted(arr) # 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 sys # used for edge case with sys.maxsize and -sys.maxsize-1 # 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 # 3.62μs -> 2.88μs (26.1% faster) def test_sorter_single_element(): # Test sorting a single-element list arr = [42] codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.96μs -> 2.92μs (35.7% faster) 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 # 4.54μs -> 2.96μs (53.5% faster) def test_sorter_reverse_sorted(): # Test sorting a reverse-sorted list arr = [5, 4, 3, 2, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.00μs (63.9% faster) def test_sorter_duplicates(): # Test sorting a list with duplicate values arr = [3, 1, 2, 3, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 2.96μs (54.9% faster) def test_sorter_negative_numbers(): # Test sorting a list with negative numbers arr = [0, -1, -3, 2, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 3.00μs (62.5% faster) def test_sorter_mixed_signs(): # Test sorting a list with both positive and negative numbers arr = [-2, 4, 0, -1, 3] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 3.08μs (59.5% faster) 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 # 4.21μs -> 2.92μs (44.3% faster) def test_sorter_floats(): # Test sorting a list with floating point numbers arr = [3.2, 1.5, 2.7, 0.1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.75μs -> 3.58μs (60.4% faster) def test_sorter_integers_and_floats(): # Test sorting a list with both integers and floats arr = [3, 1.2, 2, 4.5] codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 3.54μs (54.1% faster) # ---------------- Edge Test Cases ---------------- def test_sorter_min_max_integers(): # Test sorting a list with min and max integer values arr = [sys.maxsize, -sys.maxsize-1, 0, 100, -100] codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.21μs -> 3.46μs (79.6% faster) def test_sorter_large_and_small_floats(): # Test sorting a list with very large and very small floats arr = [1e308, 1e-308, -1e308, -1e-308, 0.0] codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.96μs -> 5.67μs (40.4% faster) def test_sorter_repeated_extremes(): # Test sorting a list with repeated extreme values arr = [float('inf'), float('-inf'), 0, float('inf'), float('-inf')] codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 3.67μs (63.7% faster) def test_sorter_nan_values(): # Test sorting a list with NaN values (NaN is not comparable, so result is implementation dependent) arr = [float('nan'), 1, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.21μs (42.9% faster) def test_sorter_empty_string(): # Test sorting a list with an empty string (should raise TypeError) arr = [1, ""] with pytest.raises(TypeError): sorter(arr.copy()) # 2.71μs -> 1.96μs (38.4% faster) def test_sorter_mixed_types(): # Test sorting a list with incompatible types (should raise TypeError) arr = [1, "a", 2] with pytest.raises(TypeError): sorter(arr.copy()) # 2.33μs -> 1.83μs (27.3% faster) def test_sorter_nested_lists(): # Test sorting a list with nested lists (should raise TypeError) arr = [1, [2], 3] with pytest.raises(TypeError): sorter(arr.copy()) # 2.58μs -> 1.88μs (37.8% faster) def test_sorter_none_value(): # Test sorting a list with None (should raise TypeError) arr = [1, None, 3] with pytest.raises(TypeError): sorter(arr.copy()) # 2.33μs -> 1.83μs (27.3% faster) def test_sorter_mutation(): # Test that the input list is mutated (bubble sort is in-place) arr = [3, 2, 1] arr_copy = arr.copy() sorter(arr) # 4.58μs -> 2.96μs (54.9% faster) # ---------------- Large Scale Test Cases ---------------- def test_sorter_large_sorted(): # Test sorting a large already sorted list arr = list(range(1000)) codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.5ms -> 29.8μs (62162% faster) def test_sorter_large_reverse(): # Test sorting a large reverse-sorted list arr = list(range(999, -1, -1)) codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.8ms -> 29.0μs (106001% faster) def test_sorter_large_random(): # Test sorting a large random list arr = random.sample(range(-10000, -9000), 1000) expected = sorted(arr) codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.2ms -> 61.1μs (45994% faster) 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 # 25.0ms -> 48.6μs (51427% faster) def test_sorter_large_floats(): # Test sorting a large list of floats arr = [random.uniform(-1000, 1000) for _ in range(1000)] expected = sorted(arr) codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.5ms -> 280μs (9718% faster) def test_sorter_large_negative(): # Test sorting a large list of negative numbers arr = [random.randint(-10000, -1) for _ in range(1000)] expected = sorted(arr) codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.7ms -> 63.1μs (43838% faster) def test_sorter_large_all_equal(): # Test sorting a large list where all elements are equal arr = [42] * 1000 codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.2ms -> 27.9μs (65053% 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-mdz07axo and push.