⚡️ Speed up function sorter by 208,955%

[codeflash-ai]

📄 208,955% (2,089.55x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 5.84 seconds → 2.79 milliseconds (best of 298 runs)

📝 Explanation and details

What changed:

• Replaced the Python-level bubble sort (two nested loops with element-by-element swaps) with the built-in list.sort().

Why it’s faster:

• Algorithmic complexity: Bubble sort is O(n^2) regardless of input; list.sort() (Timsort) is O(n log n) on average/worst and O(n) on partially/fully sorted data. This eliminates the quadratic work visible in the profiler (tens of billions of loop iterations and ~31M swaps).
• C implementation: list.sort() runs in optimized C, avoiding Python interpreter overhead from tight loops, repeated indexing, comparisons, and assignments. The original spends the majority of time in Python bytecode; the optimized version moves that work into C. The profiler shows the remaining time dominated by printing, not sorting.
• Fewer operations: No repeated range/len evaluations or per-iteration bounds checks at Python level; comparisons and moves are batched internally by Timsort.

Behavioral considerations:

• In-place semantics preserved: arr.sort() sorts in place; function still returns arr, matching the original’s side effects and return.
• Stability preserved: Python’s sort is stable, equivalent to the original adjacent-swap behavior for equal keys.
• Type behavior: Mixed int/float works; int/str raises TypeError in both versions (raised within sort), matching tests.

Evidence from tests and profiles:

• Line profiler: Original spends ~50s, dominated by nested loops and swaps; optimized total ~3.6ms with sorting itself taking microseconds; printing is now the majority of time.
• Large inputs: Massive gains on already sorted, reverse, random, and duplicates (20k%–97k% faster). Timsort’s O(n) on runs explains the exceptional speedups on sorted/partially-sorted and duplicate-heavy cases. Even worst-case random inputs drop from tens of ms to ~100–200µs.

Best-suited cases:

• Large lists, especially already sorted, nearly sorted, or with long runs/duplicates.
• All general numeric lists (ints/floats) as in tests.

Net: The speedup comes from replacing an O(n^2) Python-loop algorithm with an O(n log n)/O(n) C-optimized sort, slashing interpreter overhead and operation count.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 51 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	11.2ms	28.4μs	39420%✅
test_bubble_sort.py::test_sort	1.42s	249μs	569695%✅
test_bubble_sort_conditional.py::test_sort	8.33μs	4.42μs	88.7%✅
test_bubble_sort_import.py::test_sort	1.42s	250μs	568540%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	1.42s	249μs	571204%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	892ms	247μs	360537%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	144μs	30.0μs	380%✅

🌀 Generated Regression Tests and Runtime

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 should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 4.33μs (29.8% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.25μs -> 4.12μs (51.5% faster)

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted ascending
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 4.33μs (29.8% faster)

def test_sorter_basic_duplicates():
    # List with duplicates should be sorted, duplicates preserved
    arr = [2, 3, 2, 1, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.08μs -> 4.12μs (47.5% faster)

def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 4.21μs (10.9% faster)

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.04μs -> 4.21μs (19.8% faster)

def test_sorter_basic_two_elements_sorted():
    # Two element list, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 4.12μs (15.2% faster)

# --- Edge Test Cases ---

def test_sorter_edge_empty():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.29μs -> 4.00μs (7.30% faster)

def test_sorter_edge_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 4.17μs (27.0% faster)

def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, -4, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.92μs -> 4.46μs (32.7% faster)

def test_sorter_edge_mixed_signs():
    # List with both negative and positive numbers
    arr = [5, -10, 3, 0, -2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.25μs -> 4.42μs (41.5% faster)

def test_sorter_edge_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 # 7.88μs -> 5.33μs (47.7% faster)

def test_sorter_edge_mixed_int_float():
    # List with both ints and floats
    arr = [3, 2.2, 5, 4.4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.46μs -> 5.12μs (45.5% faster)

def test_sorter_edge_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [1e10, -1e10, 1e-10, -1e-10, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.12μs -> 6.42μs (42.2% faster)

def test_sorter_edge_already_sorted():
    # List that is already sorted, but with mixed types (int, float)
    arr = [-2, 0, 0.5, 2, 3.3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.83μs -> 5.12μs (33.3% faster)

def test_sorter_edge_stability():
    # Test stability: equal elements should retain their original order
    arr = [(2, 'a'), (1, 'b'), (2, 'c'), (1, 'd')]
    # Sorting by first element only
    # Since sorter expects numbers, we test stability with numbers only
    arr = [2, 1, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.88μs -> 4.08μs (43.9% 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 # 32.0ms -> 52.2μs (61276% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.1ms -> 52.3μs (97542% faster)

def test_sorter_large_random():
    # Large random list
    import random
    arr = list(range(1000))
    random.seed(42)  # Deterministic shuffle
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 45.1ms -> 116μs (38598% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [3] * 250 + [7] * 250
    expected = [3] * 250 + [5] * 500 + [7] * 250
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 36.6ms -> 50.1μs (72842% faster)

def test_sorter_large_negative_positive():
    # Large list with negative and positive numbers
    arr = list(range(-500, 500))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 32.0ms -> 52.5μs (60945% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [float(i) / 10 for i in range(1000)]
    import random
    random.seed(43)
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 44.2ms -> 215μs (20435% faster)

def test_sorter_large_mixed_types():
    # Large list of ints and floats mixed
    arr = [i if i % 2 == 0 else float(i) for i in range(1000)]
    import random
    random.seed(44)
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 57.2ms -> 231μs (24612% 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 list should remain unchanged
    input_list = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(input_list.copy()) # 6.96μs -> 4.58μs (51.8% faster)

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted in ascending order
    input_list = [5, 3, 1, 4, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(input_list.copy()) # 6.67μs -> 4.46μs (49.5% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should be sorted
    input_list = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(input_list.copy()) # 6.54μs -> 4.12μs (58.6% faster)

def test_sorter_basic_duplicates():
    # List with duplicates should sort and preserve duplicates
    input_list = [2, 3, 2, 1, 3]
    expected = [1, 2, 2, 3, 3]
    codeflash_output = sorter(input_list.copy()) # 6.12μs -> 4.25μs (44.1% faster)

def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    input_list = [42]
    expected = [42]
    codeflash_output = sorter(input_list.copy()) # 4.92μs -> 4.25μs (15.7% faster)

def test_sorter_basic_two_elements():
    # Two element unsorted list should be sorted
    input_list = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(input_list.copy()) # 5.04μs -> 4.08μs (23.5% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers should be sorted correctly
    input_list = [-3, -1, -2, 0, 2]
    expected = [-3, -2, -1, 0, 2]
    codeflash_output = sorter(input_list.copy()) # 5.79μs -> 4.42μs (31.1% faster)

def test_sorter_basic_mixed_signs():
    # List with positive and negative numbers
    input_list = [0, -1, 1, -2, 2]
    expected = [-2, -1, 0, 1, 2]
    codeflash_output = sorter(input_list.copy()) # 6.00μs -> 4.33μs (38.5% faster)

# --- EDGE TEST CASES ---

def test_sorter_edge_empty_list():
    # Empty list should remain unchanged
    input_list = []
    expected = []
    codeflash_output = sorter(input_list.copy()) # 4.29μs -> 3.92μs (9.60% faster)

def test_sorter_edge_all_identical():
    # All identical elements should remain unchanged
    input_list = [7, 7, 7, 7, 7]
    expected = [7, 7, 7, 7, 7]
    codeflash_output = sorter(input_list.copy()) # 5.67μs -> 4.25μs (33.3% faster)

def test_sorter_edge_large_negative():
    # List with large negative numbers
    input_list = [-999999, -1000000, -1234567]
    expected = [-1234567, -1000000, -999999]
    codeflash_output = sorter(input_list.copy()) # 6.12μs -> 4.54μs (34.9% faster)

def test_sorter_edge_floats():
    # List with floats should be sorted numerically
    input_list = [3.2, 1.5, 2.8, 1.5]
    expected = [1.5, 1.5, 2.8, 3.2]
    codeflash_output = sorter(input_list.copy()) # 7.62μs -> 5.25μs (45.2% faster)

def test_sorter_edge_mixed_int_float():
    # List with ints and floats should sort correctly
    input_list = [3, 2.5, 1, 3.5, 2]
    expected = [1, 2, 2.5, 3, 3.5]
    codeflash_output = sorter(input_list.copy()) # 7.67μs -> 5.00μs (53.3% faster)

def test_sorter_edge_large_positive():
    # List with large positive numbers
    input_list = [999999, 1000000, 1234567]
    expected = [999999, 1000000, 1234567]
    codeflash_output = sorter(input_list.copy()) # 5.46μs -> 4.33μs (26.0% faster)

def test_sorter_edge_min_max():
    # List with both min and max possible integer values
    import sys
    input_list = [sys.maxsize, -sys.maxsize - 1, 0]
    expected = [-sys.maxsize - 1, 0, sys.maxsize]
    codeflash_output = sorter(input_list.copy()) # 5.96μs -> 4.58μs (30.0% faster)

def test_sorter_edge_already_sorted_with_duplicates():
    # Already sorted list with duplicates
    input_list = [1, 2, 2, 3, 3, 4, 5]
    expected = [1, 2, 2, 3, 3, 4, 5]
    codeflash_output = sorter(input_list.copy()) # 6.17μs -> 4.38μs (41.0% faster)

def test_sorter_edge_reverse_sorted_with_duplicates():
    # Reverse sorted list with duplicates
    input_list = [5, 4, 4, 3, 2, 2, 1]
    expected = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(input_list.copy()) # 7.33μs -> 4.54μs (61.5% faster)

def test_sorter_edge_large_range():
    # List with a wide range of numbers
    input_list = [-1000, 0, 1000, -500, 500]
    expected = [-1000, -500, 0, 500, 1000]
    codeflash_output = sorter(input_list.copy()) # 6.58μs -> 4.50μs (46.3% faster)


def test_sorter_edge_mixed_types():
    # List with mixed types (int and str) should raise TypeError
    input_list = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(input_list.copy()) # 3.75μs -> 3.17μs (18.4% faster)

# --- LARGE SCALE TEST CASES ---

def test_sorter_large_already_sorted():
    # Large already sorted list
    input_list = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(input_list.copy()) # 32.2ms -> 53.0μs (60723% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    input_list = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(input_list.copy()) # 51.0ms -> 52.5μs (96934% faster)

def test_sorter_large_random():
    # Large random list
    import random
    input_list = list(range(1000))
    random.shuffle(input_list)
    expected = list(range(1000))
    codeflash_output = sorter(input_list.copy()) # 45.5ms -> 117μs (38749% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    input_list = [5] * 500 + [3] * 500
    expected = [3] * 500 + [5] * 500
    codeflash_output = sorter(input_list.copy()) # 42.2ms -> 50.1μs (84017% faster)

def test_sorter_large_negative_positive():
    # Large list with both negative and positive numbers
    input_list = list(range(-500, 500))
    expected = list(range(-500, 500))
    # Shuffle to unsort
    import random
    random.shuffle(input_list)
    codeflash_output = sorter(input_list.copy()) # 45.2ms -> 114μs (39553% faster)

def test_sorter_large_floats():
    # Large list of floats
    input_list = [float(i) / 10 for i in range(1000)]
    expected = sorted(input_list)
    import random
    random.shuffle(input_list)
    codeflash_output = sorter(input_list.copy()) # 44.4ms -> 219μs (20138% faster)

def test_sorter_large_all_identical():
    # Large list of identical elements
    input_list = [7] * 1000
    expected = [7] * 1000
    codeflash_output = sorter(input_list.copy()) # 31.8ms -> 49.5μs (64209% faster)

def test_sorter_large_sorted_with_duplicates():
    # Large sorted list with duplicates
    input_list = [i // 2 for i in range(1000)]
    expected = sorted(input_list)
    codeflash_output = sorter(input_list.copy()) # 32.0ms -> 51.5μs (62104% faster)

def test_sorter_large_edge_values():
    # Large list with edge integer values
    import sys
    input_list = [sys.maxsize, -sys.maxsize - 1] * 500
    expected = sorted(input_list)
    codeflash_output = sorter(input_list.copy()) # 40.8ms -> 99.1μs (41080% 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-mh1m28u4 and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #846 by aseembits93 was closed.