⚡️ Speed up function sorter by 320,608%

[codeflash-ai]

📄 320,608% (3,206.08x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.28 seconds → 1.02 milliseconds (best of 250 runs)

📝 Explanation and details

Optimization Summary

The optimized code achieves a 3,206x speedup (from 3.28 seconds to 1.02 milliseconds) by replacing the O(n²) bubble sort algorithm with Python's built-in Timsort via arr.sort(), which runs in O(n log n) time.

Key Changes

Algorithmic replacement:

• Original: Nested loops iterating ~61 million times for moderate-sized lists, with element-by-element swaps
• Optimized: Single arr.sort() call leveraging Python's highly-optimized C implementation of Timsort

Fallback handling:

• Added try/except to handle edge cases where arr might not support .sort() (e.g., custom sequence types)
• Fallback creates a temporary sorted list and writes values back element-by-element to preserve in-place mutation semantics

Why This Is Faster

1. Complexity reduction: O(n²) → O(n log n) means dramatically fewer operations as list size grows
2. Native optimization: Python's list.sort() is implemented in highly-optimized C code, versus interpreted Python loops
3. Memory efficiency: Timsort is an in-place algorithm with minimal overhead, while the original performs redundant comparisons and swaps

The line profiler shows the original spent 42.4 seconds across 61.7 million loop iterations, while the optimized version completes in 0.0017 seconds with just the arr.sort() call.

Test Results Analysis

All test cases show significant speedups:

• Small lists (2-10 elements): 20-197% faster (microseconds → microseconds)
• Medium lists (100 elements): 3,017-5,586% faster
• Large lists (500-1000 elements): 14,174-122,233% faster

The optimization particularly excels with larger datasets—exactly where the O(n²) vs O(n log n) difference matters most. The test_full_bubble_coverage.py reference shows this function is tested with 5,000-element lists, meaning production usage would see massive benefits.

Impact on Existing Workloads

Based on function_references, the sorter function is called from:

1. sort_from_another_file: Direct pass-through will see full speedup benefits
2. test_full_bubble_coverage.py: Tests with 5,000-element lists—these will complete orders of magnitude faster
3. compute_and_sort: Calls sorter(arr.copy()) as part of data processing pipeline—sorting overhead becomes negligible

All references preserve the in-place mutation contract and return value, so the optimization is a drop-in replacement with no behavioral changes except dramatically reduced execution time.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 63 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Click to see Existing Unit Tests

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
benchmarks/test_benchmark_bubble_sort.py::test_sort2	6.96ms	14.0μs	49436%✅
test_bubble_sort.py::test_sort	858ms	135μs	634803%✅
test_bubble_sort_conditional.py::test_sort	3.88μs	1.54μs	151%✅
test_bubble_sort_import.py::test_sort	856ms	136μs	627310%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	855ms	137μs	623073%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	527ms	137μs	384412%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	85.0μs	11.3μs	653%✅

🌀 Click to see Generated Regression Tests

import random  # used to create deterministic large-scale test data

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

def test_basic_sorting_three_elements():
    # Simple unsorted list should be sorted in ascending order
    data = [3, 1, 2]
    # Call the function under test
    codeflash_output = sorter(data); result = codeflash_output # 2.71μs -> 1.54μs (75.6% faster)

def test_already_sorted_remains_same_order():
    # An already sorted list should remain unchanged (still sorted)
    data = [0, 1, 2, 3, 4]
    before_id = id(data)
    codeflash_output = sorter(data); result = codeflash_output # 2.83μs -> 1.54μs (83.7% faster)

def test_empty_and_single_element_lists():
    # Empty list case
    empty = []
    codeflash_output = sorter(empty); res_empty = codeflash_output # 1.92μs -> 1.33μs (43.7% faster)

    # Single element list case
    single = [42]
    codeflash_output = sorter(single); res_single = codeflash_output # 1.79μs -> 1.25μs (43.3% faster)

def test_duplicates_preserved_count_and_ordering_by_value():
    # List with many duplicates should result in grouped duplicates
    data = [2, 1, 2, 1, 3, 2]
    codeflash_output = sorter(data); res = codeflash_output # 3.21μs -> 1.71μs (87.9% faster)

def test_negative_numbers_and_floats_together():
    # A mix of ints and floats should be sortable (ints and floats are comparable)
    data = [3.5, -1, 2, -1.5, 0]
    codeflash_output = sorter(data); res = codeflash_output # 4.54μs -> 2.00μs (127% faster)

def test_incompatible_types_raise_type_error():
    # Mixing incomparable types like int and str should raise TypeError during comparison
    data = [1, "a"]  # int > str is not supported in Python 3
    with pytest.raises(TypeError):
        sorter(data) # 2.08μs -> 1.58μs (31.6% faster)

def test_reverse_sorted_list():
    # A reverse-sorted list should become ascending
    data = list(range(10, 0, -1))  # 10..1
    codeflash_output = sorter(data); res = codeflash_output # 5.04μs -> 1.71μs (195% faster)

def test_function_prints_status_messages(capsys):
    # Ensure the function prints both the "Sorting list" and the final result line
    data = [2, 1]
    codeflash_output = sorter(data); res = codeflash_output
    # Capture stdout/stderr produced during the call
    captured = capsys.readouterr()

def test_large_random_integer_list_sorted_correctly():
    # Generate a deterministic large list of integers (size kept below 1000 per instructions)
    rng = random.Random(0)  # deterministic seed for reproducible tests
    size = 500  # large but under the 1000-element guideline
    data = [rng.randint(-10000, 10000) for _ in range(size)]
    # Make a copy for verification (since sorter sorts in-place)
    expected = sorted(data)
    # Call the sorter
    codeflash_output = sorter(data); res = codeflash_output # 6.64ms -> 28.4μs (23290% faster)

def test_many_duplicates_large_scale():
    # Create a list with many repeated values to test behavior with low-cardinality large data
    rng = random.Random(1)
    size = 400  # still under 1000
    # Only three distinct values but many repetitions
    pool = [5, 3, -2]
    data = [rng.choice(pool) for _ in range(size)]
    expected = sorted(data)
    codeflash_output = sorter(data); res = codeflash_output # 3.41ms -> 18.0μs (18831% faster)
    # Check counts for each distinct value to ensure no element lost
    for val in pool:
        pass

def test_sort_preserves_multiset_of_elements():
    # Ensure that sort does not add or remove elements (multiset equality)
    rng = random.Random(2)
    data = [rng.randint(-50, 50) for _ in range(200)]
    original_counts = {x: data.count(x) for x in set(data)}
    codeflash_output = sorter(data); res = codeflash_output # 854μs -> 12.1μs (6951% faster)
    # After sorting, all counts should match original_counts
    for value, count in original_counts.items():
        pass

def test_all_elements_identical():
    data = [7] * 50  # 50 identical elements
    codeflash_output = sorter(data); res = codeflash_output # 36.0μs -> 2.58μs (1295% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import io
import sys

import pytest
from code_to_optimize.bubble_sort import sorter

def test_basic_small_unsorted_list():
    """Test sorting a small unsorted list of integers."""
    codeflash_output = sorter([3, 1, 2]); result = codeflash_output # 2.79μs -> 1.50μs (86.1% faster)

def test_basic_already_sorted_list():
    """Test sorting a list that is already sorted."""
    codeflash_output = sorter([1, 2, 3, 4, 5]); result = codeflash_output # 2.54μs -> 1.58μs (60.6% faster)

def test_basic_reverse_sorted_list():
    """Test sorting a list in reverse order."""
    codeflash_output = sorter([5, 4, 3, 2, 1]); result = codeflash_output # 2.92μs -> 1.54μs (89.3% faster)

def test_basic_list_with_duplicates():
    """Test sorting a list containing duplicate values."""
    codeflash_output = sorter([3, 1, 2, 1, 3]); result = codeflash_output # 2.62μs -> 1.54μs (70.2% faster)

def test_basic_list_with_negative_numbers():
    """Test sorting a list with negative numbers."""
    codeflash_output = sorter([3, -1, 2, -5, 0]); result = codeflash_output # 2.79μs -> 1.62μs (71.8% faster)

def test_basic_list_with_float_values():
    """Test sorting a list of floating point numbers."""
    codeflash_output = sorter([3.5, 1.2, 2.8, 0.1]); result = codeflash_output # 3.58μs -> 2.00μs (79.1% faster)

def test_basic_two_element_list():
    """Test sorting a list with exactly two elements."""
    codeflash_output = sorter([2, 1]); result = codeflash_output # 2.08μs -> 1.50μs (38.9% faster)

def test_basic_two_element_sorted_list():
    """Test sorting a two-element list already in sorted order."""
    codeflash_output = sorter([1, 2]); result = codeflash_output # 1.96μs -> 1.46μs (34.3% faster)

def test_basic_mixed_positive_negative_floats():
    """Test sorting a list with mixed positive, negative, and float values."""
    codeflash_output = sorter([3.5, -1.2, 2, -5, 0.0]); result = codeflash_output # 4.17μs -> 2.04μs (104% faster)

def test_edge_single_element_list():
    """Test sorting a list with a single element."""
    codeflash_output = sorter([42]); result = codeflash_output # 1.83μs -> 1.46μs (25.7% faster)

def test_edge_empty_list():
    """Test sorting an empty list."""
    codeflash_output = sorter([]); result = codeflash_output # 1.71μs -> 1.33μs (28.2% faster)

def test_edge_all_same_elements():
    """Test sorting a list where all elements are identical."""
    codeflash_output = sorter([5, 5, 5, 5, 5]); result = codeflash_output # 2.46μs -> 1.54μs (59.6% faster)

def test_edge_all_same_negative_elements():
    """Test sorting a list with all identical negative elements."""
    codeflash_output = sorter([-3, -3, -3]); result = codeflash_output # 2.12μs -> 1.54μs (37.9% faster)

def test_edge_all_same_zero_elements():
    """Test sorting a list with all zero elements."""
    codeflash_output = sorter([0, 0, 0, 0]); result = codeflash_output # 2.21μs -> 1.50μs (47.2% faster)

def test_edge_large_numbers():
    """Test sorting a list with very large numbers."""
    codeflash_output = sorter([1000000, 1, 500000, 999999]); result = codeflash_output # 2.83μs -> 1.62μs (74.3% faster)

def test_edge_very_small_numbers():
    """Test sorting a list with very small (close to zero) floating point numbers."""
    codeflash_output = sorter([0.0001, 0.00001, 0.001, 0.0]); result = codeflash_output # 4.62μs -> 2.25μs (106% faster)

def test_edge_negative_large_numbers():
    """Test sorting a list with large negative numbers."""
    codeflash_output = sorter([-1000000, -1, -500000, -999999]); result = codeflash_output # 2.79μs -> 1.58μs (76.2% faster)

def test_edge_extreme_range_numbers():
    """Test sorting a list with numbers spanning a very wide range."""
    codeflash_output = sorter([1000000, -1000000, 0, 1, -1]); result = codeflash_output # 3.29μs -> 1.67μs (97.4% faster)

def test_edge_many_duplicates_few_unique():
    """Test sorting a list with many duplicates and few unique values."""
    codeflash_output = sorter([2, 1, 2, 1, 2, 1, 2]); result = codeflash_output # 3.12μs -> 1.62μs (92.3% faster)

def test_edge_alternating_pattern():
    """Test sorting a list with alternating high-low pattern."""
    codeflash_output = sorter([5, 1, 5, 1, 5, 1]); result = codeflash_output # 2.88μs -> 1.58μs (81.6% faster)

def test_edge_mostly_sorted_one_outlier():
    """Test sorting a nearly sorted list with one outlier at the beginning."""
    codeflash_output = sorter([100, 1, 2, 3, 4, 5]); result = codeflash_output # 2.83μs -> 1.62μs (74.4% faster)

def test_edge_mostly_sorted_outlier_at_end():
    """Test sorting a nearly sorted list with one outlier at the end."""
    codeflash_output = sorter([1, 2, 3, 4, 5, -100]); result = codeflash_output # 3.21μs -> 1.62μs (97.4% faster)

def test_edge_list_with_only_negative_numbers():
    """Test sorting a list containing only negative numbers."""
    codeflash_output = sorter([-5, -1, -10, -3]); result = codeflash_output # 2.58μs -> 1.58μs (63.2% faster)

def test_edge_float_very_close_values():
    """Test sorting floats with very close (but distinct) values."""
    codeflash_output = sorter([1.0001, 1.0, 1.00011, 0.9999]); result = codeflash_output # 3.50μs -> 2.04μs (71.4% faster)

def test_edge_float_negative_close_values():
    """Test sorting negative floats with very close values."""
    codeflash_output = sorter([-1.0001, -1.0, -1.00011, -0.9999]); result = codeflash_output # 3.58μs -> 2.08μs (72.0% faster)

def test_edge_mixed_int_float():
    """Test sorting a list with mixed integers and floats."""
    codeflash_output = sorter([3, 1.5, 2, 0.5, 4]); result = codeflash_output # 3.67μs -> 2.00μs (83.3% faster)

def test_edge_single_negative_element():
    """Test sorting a list with a single negative element."""
    codeflash_output = sorter([-5]); result = codeflash_output # 1.79μs -> 1.46μs (22.8% faster)

def test_edge_single_zero_element():
    """Test sorting a list with a single zero element."""
    codeflash_output = sorter([0]); result = codeflash_output # 1.71μs -> 1.42μs (20.5% faster)

def test_edge_two_negatives_one_positive():
    """Test sorting a list with two negatives and one positive."""
    codeflash_output = sorter([-3, 2, -1]); result = codeflash_output # 2.38μs -> 1.58μs (50.0% faster)

def test_large_scale_100_random_elements():
    """Test sorting a list of 100 randomly ordered elements."""
    arr = [64, 34, 25, 12, 22, 11, 90, 88, 45, 50, 
           32, 15, 72, 81, 19, 27, 33, 14, 18, 41,
           77, 61, 24, 38, 29, 42, 67, 16, 39, 43,
           68, 52, 28, 7, 31, 63, 54, 59, 66, 30,
           26, 48, 60, 55, 5, 36, 57, 62, 69, 49,
           23, 2, 75, 79, 40, 53, 46, 65, 58, 85,
           44, 9, 73, 74, 3, 80, 70, 47, 17, 84,
           10, 35, 6, 51, 89, 91, 8, 20, 37, 78,
           82, 1, 4, 76, 56, 71, 21, 86, 13, 87,
           83, 92, 64, 24, 35, 12, 90, 33, 28, 14]
    codeflash_output = sorter(arr); result = codeflash_output # 205μs -> 6.58μs (3017% faster)
    expected = sorted(arr)

def test_large_scale_100_sorted_elements():
    """Test sorting a list of 100 already sorted elements."""
    arr = list(range(1, 101))
    codeflash_output = sorter(arr); result = codeflash_output # 134μs -> 4.00μs (3268% faster)

def test_large_scale_100_reverse_sorted():
    """Test sorting a list of 100 elements in reverse order."""
    arr = list(range(100, 0, -1))
    codeflash_output = sorter(arr); result = codeflash_output # 227μs -> 4.00μs (5586% faster)

def test_large_scale_500_elements_random():
    """Test sorting a list of 500 randomly distributed elements."""
    arr = [i * 7 % 500 for i in range(500)]
    codeflash_output = sorter(arr); result = codeflash_output # 5.57ms -> 16.1μs (34414% faster)
    expected = sorted(arr)

def test_large_scale_500_elements_with_duplicates():
    """Test sorting a list of 500 elements with many duplicates."""
    arr = [i % 50 for i in range(500)]
    codeflash_output = sorter(arr); result = codeflash_output # 5.53ms -> 23.4μs (23504% faster)
    expected = sorted(arr)

def test_large_scale_500_negative_elements():
    """Test sorting a list of 500 negative elements."""
    arr = [-i for i in range(1, 501)]
    codeflash_output = sorter(arr); result = codeflash_output # 7.00ms -> 14.0μs (49781% faster)
    expected = sorted(arr)

def test_large_scale_500_mixed_positive_negative():
    """Test sorting a list of 500 elements with mixed positive and negative values."""
    arr = [i - 250 for i in range(500)]
    codeflash_output = sorter(arr); result = codeflash_output # 4.27ms -> 13.9μs (30606% faster)
    expected = sorted(arr)

def test_large_scale_500_floats():
    """Test sorting a list of 500 floating point numbers."""
    arr = [i * 0.5 for i in range(500)]
    arr = [arr[i] if i % 2 == 0 else arr[499 - i] for i in range(500)]
    codeflash_output = sorter(arr); result = codeflash_output # 5.90ms -> 41.3μs (14174% faster)
    expected = sorted(arr)

def test_large_scale_800_elements_with_many_duplicates():
    """Test sorting 800 elements where most values repeat."""
    arr = [i % 20 for i in range(800)]
    codeflash_output = sorter(arr); result = codeflash_output # 16.1ms -> 35.9μs (44818% faster)
    expected = sorted(arr)

def test_large_scale_800_reverse_order():
    """Test sorting 800 elements arranged in complete reverse order."""
    arr = list(range(800, 0, -1))
    codeflash_output = sorter(arr); result = codeflash_output # 19.7ms -> 21.3μs (92374% faster)

def test_large_scale_1000_sequential_elements():
    """Test sorting 1000 elements that are already in sequential order."""
    arr = list(range(1, 1001))
    codeflash_output = sorter(arr); result = codeflash_output # 19.1ms -> 26.0μs (73413% faster)

def test_large_scale_1000_reverse_sequential():
    """Test sorting 1000 elements in reverse sequential order."""
    arr = list(range(1000, 0, -1))
    codeflash_output = sorter(arr); result = codeflash_output # 31.6ms -> 25.8μs (122233% faster)

def test_large_scale_1000_mixed_random():
    """Test sorting 1000 randomly distributed integers."""
    arr = [i * 17 % 1000 for i in range(1000)]
    codeflash_output = sorter(arr); result = codeflash_output # 26.8ms -> 45.0μs (59428% faster)
    expected = sorted(arr)

def test_large_scale_1000_alternating_pattern():
    """Test sorting 1000 elements with repeating alternating pattern."""
    arr = [1 if i % 2 == 0 else 2 for i in range(1000)]
    codeflash_output = sorter(arr); result = codeflash_output # 22.6ms -> 41.6μs (54348% faster)
    expected = sorted(arr)

def test_function_returns_sorted_list():
    """Verify that the function returns the sorted list (not None)."""
    codeflash_output = sorter([3, 1, 2]); result = codeflash_output # 2.71μs -> 1.50μs (80.5% faster)

def test_function_modifies_original_list():
    """Verify that the function modifies the original list in place."""
    original = [3, 1, 2]
    codeflash_output = sorter(original); result = codeflash_output # 2.21μs -> 1.54μs (43.3% faster)

def test_function_prints_output(capsys):
    """Verify that the function prints the expected messages."""
    sorter([3, 1, 2])
    captured = capsys.readouterr()

def test_multiple_calls_same_result():
    """Verify that multiple calls with the same input produce the same result."""
    arr1 = [5, 2, 8, 1, 9]
    arr2 = [5, 2, 8, 1, 9]
    codeflash_output = sorter(arr1); result1 = codeflash_output # 3.00μs -> 1.54μs (94.6% faster)
    codeflash_output = sorter(arr2); result2 = codeflash_output # 2.25μs -> 1.33μs (68.8% faster)

def test_sorting_preserves_all_elements():
    """Verify that sorting doesn't lose or duplicate elements."""
    original = [3, 1, 4, 1, 5, 9, 2, 6]
    original_copy = original.copy()
    codeflash_output = sorter(original); result = codeflash_output # 3.62μs -> 1.75μs (107% faster)

def test_sorted_result_is_non_decreasing():
    """Verify that the result maintains non-decreasing order."""
    arr = [7, 2, 9, 1, 5, 3, 8, 4, 6]
    codeflash_output = sorter(arr); result = codeflash_output # 4.25μs -> 1.71μs (149% faster)
    for i in range(len(result) - 1):
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

📊 Performance Profile

View detailed line-by-line performance analysis
To edit these changes git checkout codeflash/optimize-sorter-ml1hr7z0 and push.

[aseembits93]

@HeshamHM28 not able to see the line profiler results

[HeshamHM28]

@aseembits93 please check it now