⚡️ Speed up function sorter by 107,519%

[codeflash-ai]

📄 107,519% (1,075.19x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.28 seconds → 3.05 milliseconds (best of 358 runs)

📝 Explanation and details

Impact: high
Impact_explanation: Looking at this optimization report, I need to assess the impact based on the provided rubric.

Let me analyze the key factors:

Runtime Analysis:

• Original Runtime: 3.28 seconds
• Optimized Runtime: 3.05 milliseconds
• This is a massive improvement well above the 100 microsecond threshold
• The 107,518% speedup is far above the 15% threshold for high impact

Algorithmic Improvement:

• Changed from O(n²) bubble sort to O(n log n) Timsort
• This is a fundamental algorithmic complexity improvement, not just a constant factor optimization

Test Results Consistency:

• existing_tests: Shows massive speedups across all tests (25,501% to 393,283%), all well above 5%
• generated_tests: Shows consistent improvements across all test cases:
  • Small lists: 15-55% speedup (above 15% threshold)
  • Large lists: 6,000-65,000% speedup (extremely high)
  • Even the smallest improvements (15%) meet the threshold for significance

Hot Path Analysis:
From the calling function details, I can see the function is called in test cases that process large arrays (5000 elements), and it's used in computational workflows like compute_and_sort, indicating it could be in performance-critical paths.

Assessment:

• All metrics significantly exceed the thresholds for high impact
• The optimization shows consistent massive improvements across all test scenarios
• The algorithmic complexity improvement (O(n²) → O(n log n)) provides scalable benefits
• Runtime improvements are in the seconds-to-milliseconds range, not microseconds
• No test case shows performance regression or marginal improvement

This is clearly a high-impact optimization that transforms an inefficient algorithm into a highly optimized one with dramatic performance gains across all scenarios.

The optimization replaces a manual bubble sort implementation with Python's built-in arr.sort() method, delivering a massive 1,075x speedup.

Key Changes:

• Eliminated the O(n²) nested loop structure that dominated execution time (75% of original runtime)
• Replaced manual element swapping with Python's highly optimized Timsort algorithm
• Removed the early termination logic (swapped flag) which is no longer needed

Why This is Faster:
Python's list.sort() uses Timsort, a hybrid stable sorting algorithm that runs in O(n log n) time and is implemented in C. The original bubble sort has O(n²) time complexity and performs all operations in Python bytecode. The profiler shows that the nested loops and element comparisons consumed over 99% of the original execution time.

Performance by Test Case Type:

• Small lists (≤10 elements): 15-55% speedup due to reduced function call overhead
• Large sorted/nearly sorted lists: 60-72% speedup as Timsort excels at detecting existing order
• Large random/reverse-sorted lists: 6,000-65,000% speedup where the O(n²) vs O(n log n) complexity difference is most pronounced
• Lists with duplicates: 21,000-26,000% speedup as Timsort handles duplicates efficiently
• Edge cases (floats, mixed types): Consistent improvement while maintaining identical error handling behavior

The optimization maintains identical functionality including in-place sorting behavior and error handling for incomparable types.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 55 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	7.33ms	28.6μs	25501%✅
test_bubble_sort.py::test_sort	986ms	254μs	388247%✅
test_bubble_sort_conditional.py::test_sort	7.62μs	4.21μs	81.2%✅
test_bubble_sort_import.py::test_sort	989ms	251μs	393283%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	986ms	251μs	392389%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	449μs	256μs	75.1%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	36.8μs	29.3μs	25.4%✅

🌀 Generated Regression Tests and Runtime

import copy  # used to ensure input is not mutated unexpectedly
import random  # used for generating large random lists
import string  # used for string sorting tests

# 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 returns an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.54μs -> 4.08μs (35.7% faster)

def test_sorter_single_element():
    # Test sorting a single-element list returns the same list
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.46μs -> 4.08μs (33.7% faster)

def test_sorter_sorted_list():
    # Test sorting an already sorted list returns the same list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.62μs -> 4.17μs (35.0% faster)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse-sorted list returns sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.33μs -> 4.08μs (55.1% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    arr = [3, 1, 4, 1, 5, 9, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.33μs -> 4.46μs (42.0% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    arr = [2, 3, 2, 1, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.21μs -> 4.21μs (47.6% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.54μs -> 4.50μs (23.1% faster)

def test_sorter_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.83μs -> 4.33μs (34.6% faster)

def test_sorter_mixed_case_strings():
    # Test sorting a list of strings with mixed case
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.33μs -> 4.29μs (24.3% faster)

# -------------------- EDGE TEST CASES --------------------

def test_sorter_all_identical_elements():
    # Test sorting a list where all elements are identical
    arr = [7] * 10
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.38μs -> 4.38μs (22.9% faster)

def test_sorter_two_elements_sorted():
    # Test sorting two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 4.04μs (15.5% faster)

def test_sorter_two_elements_unsorted():
    # Test sorting two elements unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 4.00μs (22.9% faster)

def test_sorter_large_negative_and_positive():
    # Test sorting list with large positive and negative numbers
    arr = [999999, -1000000, 0, 12345, -54321]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.88μs -> 4.62μs (48.6% faster)

def test_sorter_floats():
    # Test sorting a list with floats
    arr = [3.2, 1.5, 4.8, 1.5, 2.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.08μs -> 5.38μs (50.4% faster)

def test_sorter_mixed_ints_floats():
    # Test sorting a list with both ints and floats
    arr = [3, 1.5, 2, 4.0, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.04μs -> 5.04μs (39.7% faster)

def test_sorter_min_max_values():
    # Test sorting list with min and max possible integer values
    arr = [0, -2**63, 2**63-1, 1, -1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.71μs -> 4.75μs (41.2% faster)

def test_sorter_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ["café", "banana", "ápple"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.38μs -> 4.62μs (37.8% faster)

def test_sorter_input_not_mutated():
    # Test that the input list is not mutated if not intended (bubble sort is in-place, so check if return is same object)
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr); result = codeflash_output # 5.33μs -> 4.00μs (33.4% faster)

def test_sorter_list_of_empty_strings():
    # Test sorting a list of empty strings
    arr = ["", "", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.83μs -> 4.04μs (19.6% faster)

def test_sorter_list_with_none_raises():
    # Test that sorting a list with None raises TypeError
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.62μs -> 2.79μs (29.8% faster)

def test_sorter_list_with_unorderable_types_raises():
    # Test that sorting a list with unorderable types (e.g., int and str) raises TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.21μs -> 2.58μs (24.2% faster)

# -------------------- LARGE SCALE TEST CASES --------------------

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list (performance check)
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 84.6μs -> 52.5μs (61.3% faster)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.0ms -> 52.4μs (64829% faster)

def test_sorter_large_random_list():
    # Test sorting a large random list of integers
    arr = random.sample(range(1000), 1000)
    arr_copy = arr.copy()
    codeflash_output = sorter(arr_copy); result = codeflash_output # 28.4ms -> 118μs (23910% faster)

def test_sorter_large_list_with_duplicates():
    # Test sorting a 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 # 24.2ms -> 93.3μs (25826% faster)

def test_sorter_large_strings():
    # Test sorting a large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr_copy); result = codeflash_output # 29.9ms -> 139μs (21332% faster)

def test_sorter_large_list_all_identical():
    # Test sorting a large list where all elements are identical
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 85.0μs -> 49.2μs (72.6% faster)

def test_sorter_large_alternating_pattern():
    # Test sorting a large list with an alternating pattern
    arr = [1, 2] * 500
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.9ms -> 84.6μs (22254% 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 test cases
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

# 1. Basic Test Cases

def test_empty_list():
    # Test sorting an empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()) # 5.75μs -> 4.04μs (42.3% faster)

def test_single_element():
    # Test sorting a list with one element
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()) # 5.62μs -> 4.12μs (36.4% faster)

def test_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 5.50μs -> 4.25μs (29.4% faster)

def test_reverse_sorted_list():
    # Test sorting a reverse sorted list
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()) # 6.25μs -> 4.17μs (50.0% faster)

def test_unsorted_list():
    # Test sorting a typical unsorted list
    arr = [3, 1, 4, 1, 5, 9, 2]
    expected = [1, 1, 2, 3, 4, 5, 9]
    codeflash_output = sorter(arr.copy()) # 6.33μs -> 4.29μs (47.6% faster)

def test_duplicates():
    # Test sorting a list with duplicate values
    arr = [2, 3, 2, 1, 3, 1]
    expected = [1, 1, 2, 2, 3, 3]
    codeflash_output = sorter(arr.copy()) # 6.42μs -> 4.21μs (52.5% faster)

def test_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-3, -1, -2, 0, 2, 1]
    expected = [-3, -2, -1, 0, 1, 2]
    codeflash_output = sorter(arr.copy()) # 5.50μs -> 4.33μs (26.9% faster)

def test_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    arr = [10, -10, 5, -5, 0]
    expected = [-10, -5, 0, 5, 10]
    codeflash_output = sorter(arr.copy()) # 6.12μs -> 4.33μs (41.3% faster)

# 2. Edge Test Cases

def test_all_identical_elements():
    # Test sorting a list where all elements are identical
    arr = [7] * 10
    expected = [7] * 10
    codeflash_output = sorter(arr.copy()) # 5.25μs -> 4.33μs (21.2% faster)

def test_two_elements_sorted():
    # Test sorting a two-element list that is already sorted
    arr = [1, 2]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 4.67μs -> 4.00μs (16.7% faster)

def test_two_elements_unsorted():
    # Test sorting a two-element list that is not sorted
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()) # 4.92μs -> 4.00μs (22.9% faster)

def test_large_negative_and_positive():
    # Test sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 1, -1]
    expected = [-sys.maxsize-1, -1, 0, 1, sys.maxsize]
    codeflash_output = sorter(arr.copy()) # 6.62μs -> 4.58μs (44.6% faster)

def test_floats_and_integers():
    # Test sorting a list with floats and integers
    arr = [3.5, 2, 4.1, 2.0, 3]
    expected = [2, 2.0, 3, 3.5, 4.1]
    codeflash_output = sorter(arr.copy()) # 8.17μs -> 5.38μs (51.9% faster)

def test_nan_and_inf():
    # Test sorting a list with float('nan'), float('inf'), float('-inf')
    arr = [float('inf'), float('-inf'), 0, 1, -1]
    # According to Python's sorting, NaN always goes to the end, -inf at start, inf at end (before NaN)
    expected = [float('-inf'), -1, 0, 1, float('inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.46μs -> 4.67μs (38.4% faster)

def test_with_nan():
    # Test sorting a list with NaN values
    arr = [3, float('nan'), 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.00μs -> 4.38μs (14.3% faster)

def test_with_inf_and_nan():
    # Test sorting a list with both inf and nan
    arr = [float('nan'), float('inf'), -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.75μs -> 4.46μs (29.0% faster)

def test_mutation_of_input():
    # Test that the function sorts in-place (bubble sort mutates the input)
    arr = [5, 4, 3]
    sorter(arr) # 5.46μs -> 4.17μs (31.0% faster)

def test_large_small_floats():
    # Test sorting with very large and very small floats
    arr = [1e308, 1e-308, -1e308, -1e-308, 0.0]
    expected = [-1e308, -1e-308, 0.0, 1e-308, 1e308]
    codeflash_output = sorter(arr.copy()) # 10.5μs -> 7.17μs (45.9% faster)

def test_strings_raise_type_error():
    # Test that sorting a list with non-comparable types raises TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.58μs -> 2.67μs (34.4% faster)

def test_none_in_list():
    # Test that sorting a list with None and ints raises TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 3.17μs -> 2.62μs (20.6% faster)

# 3. Large Scale Test Cases

def test_large_sorted_list():
    # Test sorting a large already sorted list (performance and correctness)
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 84.3μs -> 52.5μs (60.6% faster)

def test_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 34.0ms -> 52.4μs (64716% faster)

def test_large_random_list():
    # Test sorting a large random list
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 28.1ms -> 116μs (24008% faster)

def test_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()) # 24.2ms -> 91.2μs (26432% faster)

def test_large_negative_numbers():
    # 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()) # 26.9ms -> 114μs (23361% faster)

def test_large_floats():
    # Test sorting a large list of floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 27.2ms -> 415μs (6446% faster)

def test_large_mixed_types():
    # Test sorting a large list with both ints and floats
    arr = [random.randint(-1000, 1000) if i % 2 == 0 else random.uniform(-1000, 1000) for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 33.1ms -> 365μs (8972% 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-mgu7rqxv and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #824 by aseembits93 was closed.