⚡️ Speed up function `sorter` by 87% #530

codeflash-ai · 2025-07-08T23:25:59Z

📄 87% (0.87x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`

⏱️ Runtime : 3.46 seconds → 1.85 seconds (best of 5 runs)

📝 Explanation and details

Certainly! Your code is a classic bubble sort implementation with two major performance issues.

It never checks if the array is already sorted in a pass, so it does the maximum number of iterations.
It redundantly computes len(arr) every inner-loop.
Bubble sort is inherently slow — but you require an in-place sort and the result must be the same, so we can improve with some key optimizations.

Optimized Bubble Sort

Improvements.

Early exit: Stop if no swaps occurred (array is sorted).
Reduce inner loop range each time: The last elements are already sorted after each pass.
Direct tuple-swap: Use Python's multiple assignment swap for speed.
Pre-compute len(arr): Only compute once.

Here is the optimized version preserving all your prints and output.

Explanation of changes

swapped check: If no swaps in an outer pass, break for a big speed boost on nearly-sorted data.
range(n - i - 1): Each pass pushes the next-largest value into place, so inner loop shortens.
Tuple swap: Faster than three assignments.
No unnecessary calls to len() in loop.

This is the fastest bubble-sort possible without changing the algorithm (still in-place, correct result).

If you want even faster code, and are allowed to use Python's built-in sort, you could simply use arr.sort(), but as per your request, this implements only the algorithmic optimization of what you already have.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 57 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`	6.99ms	4.31ms	✅62.0%
`test_bubble_sort.py::test_sort`	866ms	561ms	✅54.4%
`test_bubble_sort_conditional.py::test_sort`	6.17μs	5.58μs	✅10.4%
`test_bubble_sort_import.py::test_sort`	861ms	555ms	✅55.0%
`test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class`	867ms	560ms	✅54.7%
`test_bubble_sort_parametrized.py::test_sort_parametrized`	512ms	257μs	✅199018%
`test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized`	104μs	28.9μs	✅261%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists
import string  # used for string sorting tests
import sys  # used for testing 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_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([])

def test_sorter_single_element():
    # Test sorting a list with a single element
    codeflash_output = sorter([42])

def test_sorter_two_elements_sorted():
    # Test sorting a two-element list that is already sorted
    codeflash_output = sorter([1, 2])

def test_sorter_two_elements_unsorted():
    # Test sorting a two-element list that is not sorted
    codeflash_output = sorter([2, 1])

def test_sorter_multiple_elements_sorted():
    # Test sorting a list that is already sorted
    codeflash_output = sorter([1, 2, 3, 4, 5])

def test_sorter_multiple_elements_reverse():
    # Test sorting a list that is reverse sorted
    codeflash_output = sorter([5, 4, 3, 2, 1])

def test_sorter_multiple_elements_random():
    # Test sorting a list with random order
    codeflash_output = sorter([3, 1, 4, 5, 2])

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = sorter([2, 3, 2, 1, 3])

def test_sorter_with_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([0, -1, 3, -2, 2])

def test_sorter_with_all_equal_elements():
    # Test sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7])

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

def test_sorter_with_large_and_small_integers():
    # Test sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_with_floats():
    # Test sorting a list of floating point numbers
    arr = [3.14, 2.71, -1.0, 0.0, 2.71]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_with_mixed_ints_and_floats():
    # Test sorting a list with both ints and floats
    arr = [1, 2.5, 2, 1.5, 0]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_with_strings():
    # Test sorting a list of strings
    arr = ['banana', 'apple', 'cherry', 'date']
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_with_empty_strings():
    # Test sorting a list with empty strings
    arr = ['', 'a', 'abc', '', 'b']
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_with_unicode_strings():
    # Test sorting a list with Unicode strings
    arr = ['café', 'cafe', 'cafè', 'cäfe']
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_with_booleans():
    # Test sorting a list with boolean values
    arr = [True, False, True, False]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_with_all_types_raises():
    # Test that sorting a list with incomparable types raises TypeError
    arr = [1, "a", None]
    with pytest.raises(TypeError):
        sorter(arr[:])

def test_sorter_with_nested_lists_raises():
    # Test that sorting a list with nested lists raises TypeError
    arr = [1, [2, 3], 4]
    with pytest.raises(TypeError):
        sorter(arr[:])

def test_sorter_stability():
    # Test that sorter is stable (preserves order of equal elements)
    class Item:
        def __init__(self, value, label):
            self.value = value
            self.label = label
        def __lt__(self, other):
            return self.value < other.value
        def __eq__(self, other):
            return self.value == other.value and self.label == other.label
        def __repr__(self):
            return f"Item({self.value}, {self.label})"
    arr = [Item(1, 'a'), Item(2, 'b'), Item(1, 'c'), Item(2, 'd')]
    codeflash_output = sorter(arr[:]); sorted_arr = codeflash_output

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

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr[:])

def test_sorter_large_reverse_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr[:])

def test_sorter_large_random_list():
    # Test sorting a large list with random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_large_list_with_duplicates():
    # Test sorting a large list with many duplicate values
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_large_list_of_strings():
    # Test sorting a large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

def test_sorter_performance_on_large_list():
    # Test that sorter completes on a large list within reasonable time (not a strict timing test)
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:])

# ------------------ ADDITIONAL EDGE CASES ------------------

def test_sorter_mutates_input():
    # Test that sorter mutates the input list in-place
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output

def test_sorter_with_none_raises():
    # Test that passing None raises TypeError
    with pytest.raises(TypeError):
        sorter(None)

def test_sorter_with_non_list_iterable():
    # Test that passing a tuple raises AttributeError (since tuples don't support item assignment)
    arr = (3, 2, 1)
    with pytest.raises(TypeError):
        sorter(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 string  # used for string sorting tests
import sys  # used for edge value tests

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

# unit tests

# -----------------------
# 1. 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 # 4.58μs -> 4.04μs (13.4% 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 # 5.04μs -> 4.83μs (4.30% faster)

def test_sorter_basic_unsorted():
    # Random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 4.54μs (4.58% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.67μs -> 4.58μs (1.83% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-1, -3, 2, 0, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 4.54μs (4.58% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.88μs -> 4.00μs (3.12% slower)

def test_sorter_basic_two_elements():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.12μs -> 4.00μs (3.12% faster)

def test_sorter_basic_floats():
    # List with floats
    arr = [3.1, 2.2, 5.5, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.67μs -> 5.42μs (4.62% faster)

def test_sorter_basic_mixed_int_float():
    # List with ints and floats
    arr = [2, 1.5, 3, 0.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.92μs -> 5.62μs (5.17% faster)

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 4.71μs (0.892% faster)

# -----------------------
# 2. Edge Test Cases
# -----------------------

def test_sorter_edge_empty():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.50μs -> 3.58μs (2.34% slower)

def test_sorter_edge_all_equal():
    # All elements are equal
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.50μs -> 3.92μs (14.9% faster)

def test_sorter_edge_large_numbers():
    # Very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.00μs -> 5.58μs (7.45% faster)

def test_sorter_edge_large_floats():
    # Very large and very small floats
    arr = [1e308, -1e308, 0.0, 1.5e307, -1.5e307]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.08μs -> 8.21μs (10.7% faster)

def test_sorter_edge_strings_case():
    # Strings with different cases
    arr = ["Banana", "apple", "Apple", "banana"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.92μs -> 4.88μs (0.862% faster)

def test_sorter_edge_special_characters():
    # Strings with special characters
    arr = ["!exclaim", "#hash", "alpha", "Beta"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 4.62μs (5.41% faster)

def test_sorter_edge_single_characters():
    # Single-character strings
    arr = ["z", "a", "m", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.75μs -> 4.83μs (1.74% slower)

def test_sorter_edge_mixed_types():
    # Should raise TypeError for unorderable types in Python 3
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_none():
    # Should raise TypeError if None is present
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_nested_lists():
    # Should raise TypeError if lists are present (unorderable with ints)
    arr = [1, [2], 3]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_sorter_edge_empty_strings():
    # Sorting with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.88μs -> 4.71μs (3.55% faster)

# -----------------------
# 3. Large Scale Test Cases
# -----------------------

def test_sorter_large_random_ints():
    # Large list of random integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.6ms -> 16.7ms (70.7% faster)

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.7ms -> 51.2μs (36494% faster)

def test_sorter_large_reverse():
    # Large reverse-sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 31.5ms -> 20.1ms (56.8% 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 # 25.7ms -> 14.3ms (79.1% faster)

def test_sorter_large_strings():
    # Large list of random strings
    arr = [''.join(random.choices(string.ascii_letters, k=5)) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.6ms -> 17.5ms (75.1% faster)

def test_sorter_large_negative_floats():
    # Large list of negative floats
    arr = [random.uniform(-10000, 0) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 28.0ms -> 15.9ms (76.8% faster)

def test_sorter_large_already_uniform():
    # Large list, all elements the same
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 18.4ms -> 48.7μs (37695% 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-mcv5s096 and push.

Certainly! Your code is a classic **bubble sort** implementation with two major performance issues. - It never checks if the array is already sorted in a pass, so it does the maximum number of iterations. - It redundantly computes `len(arr)` every inner-loop. - Bubble sort is inherently slow — but you require an in-place sort and the result must be the same, so we can improve with some key optimizations. ### Optimized Bubble Sort Improvements. 1. **Early exit:** Stop if no swaps occurred (array is sorted). 2. **Reduce inner loop range each time:** The last elements are already sorted after each pass. 3. **Direct tuple-swap:** Use Python's multiple assignment swap for speed. 4. **Pre-compute `len(arr)`:** Only compute once. Here is the **optimized version** preserving all your prints and output. ### Explanation of changes - **`swapped` check:** If no swaps in an outer pass, break for a big speed boost on nearly-sorted data. - **`range(n - i - 1)`:** Each pass pushes the next-largest value into place, so inner loop shortens. - **Tuple swap:** Faster than three assignments. - **No unnecessary calls to `len()` in loop.** This is the fastest bubble-sort possible without changing the algorithm (still in-place, correct result). **If you want even faster code, and are allowed to use Python's built-in sort**, you could simply use `arr.sort()`, but as per your request, this implements only the algorithmic optimization of what you already have.

codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jul 8, 2025

codeflash-ai bot requested a review from aseembits93 July 8, 2025 23:26

aseembits93 closed this Jul 8, 2025

codeflash-ai bot deleted the codeflash/optimize-sorter-mcv5s096 branch July 8, 2025 23:28

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⚡️ Speed up function `sorter` by 87% #530

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⚡️ Speed up function sorter by 87% #530

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codeflash-ai bot commented Jul 8, 2025

📄 87% (0.87x) speedup for sorter in code_to_optimize/bubble_sort.py

📝 Explanation and details

Optimized Bubble Sort

Explanation of changes

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⚡️ Speed up function `sorter` by 87% #530

⚡️ Speed up function `sorter` by 87% #530

📄 87% (0.87x) speedup for `sorter` in `code_to_optimize/bubble_sort.py`