⚡️ Speed up function sorter by 185,326%

[codeflash-ai]

📄 185,326% (1,853.26x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.73 seconds → 2.01 milliseconds (best of 132 runs)

📝 Explanation and details

The optimization replaces a manual bubble sort implementation with Python's built-in arr.sort() method, delivering a 185,325% speedup.

Key Changes:

• Eliminated nested loops: The original code used O(n²) bubble sort with nested loops that performed ~113M iterations for larger inputs
• Leveraged Timsort: Python's sort() uses Timsort, an optimized hybrid algorithm with O(n log n) average complexity and O(n) best-case performance for nearly-sorted data
• Reduced function calls: Eliminated millions of array index operations and comparisons per sort

Why It's Faster:

• Algorithmic improvement: Timsort is fundamentally more efficient than bubble sort, especially as data size increases
• Native C implementation: Python's sort is implemented in optimized C code rather than interpreted Python loops
• Adaptive sorting: Timsort performs exceptionally well on real-world data patterns (partially sorted, reverse sorted, etc.)

Performance Characteristics:

• Small arrays (≤10 elements): Modest 10-45% speedup due to reduced overhead
• Large arrays (1000 elements): Dramatic 34,000-100,000% speedup where algorithmic complexity dominates
• Best performance: Already sorted or reverse-sorted large arrays benefit most from Timsort's adaptive nature
• Consistent gains: All test cases show improvement, with larger datasets seeing exponentially better performance

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 48 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	7.54ms	21.3μs	35291%✅
test_bubble_sort.py::test_sort	934ms	158μs	588720%✅
test_bubble_sort_conditional.py::test_sort	11.3μs	7.96μs	42.4%✅
test_bubble_sort_import.py::test_sort	924ms	156μs	590536%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	933ms	159μs	583507%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	576ms	155μs	370598%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	135μs	48.3μs	180%✅

🌀 Generated Regression Tests and Runtime

import random # used for generating large random lists import sys # used for edge case 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_sorted_list(): # Already sorted list should remain unchanged arr = [1, 2, 3, 4, 5] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.12μs -> 7.71μs (18.4% faster) def test_sorter_reverse_sorted_list(): # Reverse sorted list should become sorted arr = [5, 4, 3, 2, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.33μs -> 7.79μs (19.8% faster) def test_sorter_unsorted_list(): # Unsorted list should become sorted arr = [3, 1, 4, 5, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.17μs -> 7.58μs (20.9% faster) def test_sorter_with_duplicates(): # List with duplicates should be sorted and duplicates preserved arr = [3, 1, 2, 3, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 7.58μs (34.6% faster) def test_sorter_with_negative_numbers(): # List with negative numbers should be sorted correctly arr = [0, -1, 3, -2, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 7.88μs (30.2% faster) def test_sorter_with_single_element(): # Single element list should remain unchanged arr = [42] codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.71μs -> 7.62μs (14.2% faster) def test_sorter_with_two_elements_sorted(): # Two element sorted list arr = [1, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.17μs -> 7.62μs (20.2% faster) def test_sorter_with_two_elements_unsorted(): # Two element unsorted list arr = [2, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.88μs -> 7.75μs (14.5% faster) # === Edge Test Cases === def test_sorter_empty_list(): # Empty list should return empty list arr = [] codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.12μs -> 7.33μs (10.8% faster) def test_sorter_all_identical_elements(): # List with all identical elements should remain unchanged arr = [7, 7, 7, 7, 7] codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.96μs -> 7.67μs (16.9% faster) def test_sorter_large_positive_and_negative_numbers(): # 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 # 11.4μs -> 8.21μs (38.6% faster) def test_sorter_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 # 9.75μs -> 7.71μs (26.5% faster) def test_sorter_floats_and_integers(): # List with floats and integers arr = [3.5, 2, 4.1, 2.0, 3] codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.3μs -> 8.46μs (45.3% faster) def test_sorter_negative_floats(): # List with negative floats arr = [-1.1, -3.3, -2.2, 0.0] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.79μs -> 8.25μs (18.7% faster) def test_sorter_stability(): # Test that the sort is stable for equal elements class Obj: def __init__(self, value, tag): self.value = value self.tag = tag def __lt__(self, other): return self.value < other.value def __gt__(self, other): return self.value > other.value def __eq__(self, other): return self.value == other.value and self.tag == other.tag def __repr__(self): return f"Obj({self.value}, '{self.tag}')" arr = [Obj(1, 'a'), Obj(2, 'b'), Obj(1, 'c')] # sorter will not be stable for custom objects unless __lt__ and __gt__ are defined # but our implementation does not guarantee stability, so we only check for correct order codeflash_output = sorter(arr.copy()); result = codeflash_output # 13.7μs -> 11.7μs (17.1% faster) def test_sorter_mutates_input(): # Check if sorter mutates the input list (should, since it sorts in-place) arr = [2, 1] sorter(arr) # 8.67μs -> 7.75μs (11.8% faster) def test_sorter_large_range(): # List with a large range of numbers arr = list(range(-500, 501, 100)) # [-500, -400, ..., 400, 500] random.shuffle(arr) codeflash_output = sorter(arr.copy()); result = codeflash_output # 14.0μs -> 8.17μs (71.9% faster) def test_sorter_string_elements(): # List with string elements should raise TypeError arr = [1, "a", 3] with pytest.raises(TypeError): sorter(arr.copy()) # 39.0μs -> 38.0μs (2.63% faster) def test_sorter_none_element(): # List with None should raise TypeError arr = [1, None, 3] with pytest.raises(TypeError): sorter(arr.copy()) # 38.7μs -> 37.4μs (3.45% 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 # 19.9ms -> 33.9μs (58622% 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 # 33.6ms -> 34.2μs (98070% faster) def test_sorter_large_random(): # Large random list arr = random.sample(range(-10000, -9000), 1000) codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.6ms -> 86.1μs (34299% faster) def test_sorter_large_duplicates(): # Large list with many duplicates arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)] codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.6ms -> 67.6μs (40743% faster) def test_sorter_large_negative_numbers(): # Large list of negative numbers arr = [random.randint(-10000, -1) for _ in range(1000)] codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.4ms -> 82.9μs (35377% faster) def test_sorter_large_floats(): # Large list of floats arr = [random.uniform(-1000, 1000) for _ in range(1000)] codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.2ms -> 308μs (9371% faster) def test_sorter_large_alternating_signs(): # Large list with alternating positive and negative numbers arr = [i if i % 2 == 0 else -i for i in range(1000)] random.shuffle(arr) codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.3ms -> 79.3μs (36897% faster) # codeflash_output is used to check that the output of the original code is the same as that of the optimized code. #------------------------------------------------ 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 array should remain unchanged arr = [1, 2, 3, 4, 5] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.62μs -> 7.88μs (22.2% faster) def test_sorter_basic_unsorted(): # Unsorted array should be sorted in ascending order arr = [5, 3, 1, 4, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 7.79μs (33.7% faster) def test_sorter_basic_duplicates(): # Array with duplicate values arr = [3, 1, 2, 3, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.79μs -> 7.96μs (23.0% faster) def test_sorter_basic_negatives(): # Array with negative numbers arr = [-1, -3, 2, 0, -2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.96μs -> 7.79μs (27.8% faster) def test_sorter_basic_mixed_signs(): # Array with both positive and negative numbers arr = [0, -1, 1, -2, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 7.79μs (28.9% faster) # --- Edge Test Cases --- def test_sorter_edge_empty(): # Empty array should return empty arr = [] codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.38μs -> 7.62μs (9.84% faster) def test_sorter_edge_single_element(): # Single element array should return unchanged arr = [42] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.21μs -> 7.88μs (16.9% faster) def test_sorter_edge_all_equal(): # All elements equal arr = [7, 7, 7, 7] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.71μs -> 7.71μs (26.0% faster) def test_sorter_edge_already_sorted_descending(): # Array sorted in descending order arr = [5, 4, 3, 2, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 7.71μs (37.3% faster) def test_sorter_edge_large_negative(): # Array with large negative and positive values arr = [-1000000, 1000000, 0, -999999, 999999] codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 8.38μs (37.8% faster) def test_sorter_edge_floats(): # Array with float values arr = [3.2, 1.5, 2.8, 0.0] codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 8.46μs (35.0% faster) def test_sorter_edge_mixed_int_float(): # Array with both int and float values arr = [2, 1.1, 3, 2.2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.2μs -> 8.38μs (33.3% faster) def test_sorter_edge_min_max_int(): # Array with minimum and maximum Python int values arr = [-(2**31), 0, 2**31-1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.50μs -> 8.04μs (18.1% faster) def test_sorter_edge_preserve_input(): # Ensure original input is not mutated (sorter sorts in-place, so test with copy) arr = [3, 2, 1] arr_copy = arr.copy() sorter(arr_copy) # 9.75μs -> 7.67μs (27.2% faster) # --- Large Scale Test Cases --- def test_sorter_large_sorted(): # Large sorted list (performance and correctness) arr = list(range(1000)) codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.9ms -> 34.0μs (58492% faster) def test_sorter_large_reverse(): # Large reverse sorted list arr = list(range(999, -1, -1)) codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.5ms -> 33.4μs (100140% faster) def test_sorter_large_random(): # Large random list import random arr = list(range(1000)) random.shuffle(arr) codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.9ms -> 69.1μs (43127% faster) def test_sorter_large_duplicates(): # Large list with many duplicates arr = [5]*500 + [3]*250 + [7]*250 codeflash_output = sorter(arr.copy()); result = codeflash_output # 23.2ms -> 32.1μs (72208% faster) def test_sorter_large_negatives_and_positives(): # Large list with negative and positive numbers arr = list(range(-500, 500)) import random random.shuffle(arr) codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.7ms -> 70.2μs (42170% faster) def test_sorter_large_all_equal(): # Large list where all elements are equal arr = [42]*1000 codeflash_output = sorter(arr.copy()); result = codeflash_output # 19.4ms -> 33.8μs (57316% faster) # --- Mutation-sensitive test cases --- def test_sorter_mutation_sensitive(): # If sorter is mutated to sort in descending order, this will fail arr = [3, 1, 2] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.67μs -> 7.83μs (23.4% faster) def test_sorter_mutation_sensitive_duplicates_order(): # Ensure stability for duplicates (bubble sort is stable) arr = [2, 1, 2, 1] codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.38μs -> 7.75μs (21.0% 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-mg1jen9z and push.