⚡️ Speed up function sorter by 101%

[codeflash-ai]

📄 101% (1.01x) speedup for sorter in code_to_optimize/bubble_sort_codeflash_trace.py

⏱️ Runtime : 1.03 seconds → 515 milliseconds (best of 17 runs)

📝 Explanation and details

The optimized bubble sort implements three key performance improvements:

1. Reduced Inner Loop Range: The inner loop uses range(len(arr) - 1 - i) instead of range(len(arr) - 1). This avoids redundant comparisons since after each outer loop iteration, the largest i elements are already in their final sorted positions at the end of the array.

2. Early Termination: Added a swapped flag that tracks whether any swaps occurred during a pass. If no swaps happen, the array is already sorted and the algorithm breaks early. This provides massive speedups for already-sorted or partially-sorted data.

3. Tuple Swap: Replaced the 3-line temporary variable swap with Python's tuple assignment arr[j], arr[j + 1] = arr[j + 1], arr[j], which is more efficient at the bytecode level.

Performance Impact: The test results show the optimization is particularly effective for:

• Already sorted lists: 95,776% faster (55.6ms → 58.0μs) - early termination eliminates all unnecessary passes
• Lists with duplicates: 101-131% faster - fewer comparisons needed due to reduced inner loop
• Partially sorted data: 79.2% faster for already sorted with duplicates - early termination kicks in quickly

Even worst-case scenarios (reverse sorted) see 58-59% improvement due to the reduced inner loop range, while maintaining the same O(n²) complexity.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 6 Passed
🌀 Generated Regression Tests	✅ 62 Passed
⏪ Replay Tests	✅ 13 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
benchmarks_test/test_benchmark_bubble_sort_example.py::test_sort2	18.3ms	11.4ms	60.3%✅

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
from code_to_optimize.bubble_sort_codeflash_trace import sorter
# function to test
from codeflash.benchmarking.codeflash_trace import codeflash_trace

# unit tests

# ========================
# Basic Test Cases
# ========================

def test_sorter_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.12μs -> 4.24μs (44.6% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.76μs -> 5.22μs (10.5% faster)

def test_sorter_basic_unsorted():
    # Unsorted list with random order
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 4.81μs (10.1% faster)

def test_sorter_basic_duplicates():
    # List containing duplicate values
    arr = [2, 3, 2, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.19μs -> 4.94μs (5.06% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.22μs -> 3.14μs (2.52% faster)

def test_sorter_basic_two_elements():
    # List with two elements
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 3.97μs -> 3.81μs (4.30% faster)

def test_sorter_basic_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 2.83μs -> 2.57μs (10.2% faster)

# ========================
# Edge Test Cases
# ========================

def test_sorter_edge_all_equal():
    # List where all elements are equal
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.58μs -> 3.62μs (26.7% faster)

def test_sorter_edge_negative_numbers():
    # List containing negative numbers
    arr = [-3, -1, -2, -5, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.50μs -> 4.95μs (11.2% faster)

def test_sorter_edge_mixed_sign_numbers():
    # List containing both negative and positive numbers
    arr = [0, -1, 3, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.40μs -> 4.83μs (11.6% faster)

def test_sorter_edge_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [1_000_000, -1_000_000, 0, 999_999, -999_999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.02μs -> 5.31μs (13.3% faster)

def test_sorter_edge_floats():
    # List containing floating point numbers
    arr = [2.5, 3.1, 1.0, 0.5, 2.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.71μs -> 5.13μs (11.3% faster)

def test_sorter_edge_mixed_int_float():
    # List containing both integers and floats
    arr = [1, 2.2, 0, 3.3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.50μs -> 5.65μs (15.1% faster)

def test_sorter_edge_min_max_int():
    # List containing Python's min and max integer values
    import sys
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.80μs -> 4.36μs (10.1% faster)

def test_sorter_edge_already_sorted():
    # List that is already sorted, but with repeated values
    arr = [1, 2, 2, 3, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.25μs -> 3.49μs (79.2% faster)

def test_sorter_edge_single_negative():
    # List with a single negative number
    arr = [-42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 2.95μs -> 3.12μs (5.26% slower)

def test_sorter_edge_large_negative_positive():
    # List with large negative and large positive numbers
    arr = [-10**9, 10**9, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.38μs -> 4.16μs (5.43% faster)

def test_sorter_edge_sublist_sorted():
    # List where a sublist is sorted, but overall list is not
    arr = [1, 2, 3, 7, 6, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.58μs -> 4.61μs (21.0% faster)

# ========================
# Large Scale Test Cases
# ========================

def test_sorter_large_sorted():
    # Large list already sorted
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 55.6ms -> 58.0μs (95776% faster)

def test_sorter_large_reverse():
    # Large list reverse sorted
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 85.3ms -> 53.7ms (59.0% faster)

def test_sorter_large_random():
    # Large list with random order
    import random
    arr = list(range(1000))
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 75.6ms -> 46.5ms (62.5% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [1] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 71.7ms -> 35.7ms (101% faster)

def test_sorter_large_mixed():
    # Large list with mixed negative, positive, and zero
    arr = [i if i % 3 == 0 else -i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 77.1ms -> 46.2ms (67.1% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [float(i) for i in range(1000)]
    arr.reverse()
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 82.6ms -> 57.5ms (43.7% faster)

def test_sorter_large_alternating():
    # Large list with alternating values
    arr = [i if i % 2 == 0 else -i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 70.7ms -> 39.5ms (79.0% faster)

# ========================
# Mutation Testing: Ensuring Robustness
# ========================

@pytest.mark.parametrize("arr,expected", [
    ([1, 2, 3], [1, 2, 3]),            # Already sorted
    ([3, 2, 1], [1, 2, 3]),            # Reverse
    ([2, 3, 2, 1], [1, 2, 2, 3]),      # Duplicates
    ([], []),                          # Empty
    ([0], [0]),                        # Single
    ([5, 5, 5], [5, 5, 5]),            # All equal
    ([1, -1, 0], [-1, 0, 1]),          # Mixed sign
    ([1.5, 1, 2.5], [1, 1.5, 2.5]),    # Floats and ints
    ([999, -999, 0], [-999, 0, 999]),  # Large magnitude
])
def test_sorter_parametrized(arr, expected):
    # Parametrized test to ensure mutation testing robustness
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 39.2μs -> 36.7μs (6.81% 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_codeflash_trace import sorter
# function to test
from codeflash.benchmarking.codeflash_trace import codeflash_trace

# -------------------------
# Unit Tests for sorter
# -------------------------

# 1. Basic Test Cases

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 2.94μs -> 2.83μs (3.86% faster)

def test_sorter_single_element():
    # Test sorting a list with one element
    codeflash_output = sorter([42]) # 3.38μs -> 3.29μs (2.83% faster)

def test_sorter_sorted_list():
    # Test sorting a list that's already sorted
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 4.73μs -> 3.39μs (39.6% faster)

def test_sorter_reverse_sorted_list():
    # Test sorting a list sorted in reverse order
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 5.36μs -> 5.18μs (3.47% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 2, 5]) # 5.10μs -> 4.63μs (10.1% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate values
    codeflash_output = sorter([2, 3, 2, 1, 3]) # 5.18μs -> 4.89μs (5.93% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -2, 0, 2]) # 4.97μs -> 4.22μs (17.9% faster)

def test_sorter_mixed_sign_numbers():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([0, -1, 1, -2, 2]) # 5.24μs -> 4.98μs (5.22% faster)

def test_sorter_all_equal():
    # Test sorting a list where all elements are equal
    codeflash_output = sorter([7, 7, 7, 7]) # 4.21μs -> 3.45μs (22.2% faster)

def test_sorter_floats():
    # Test sorting a list of floating point numbers
    codeflash_output = sorter([3.1, 2.4, 2.5, 3.0]) # 4.95μs -> 4.51μs (9.77% faster)

def test_sorter_mixed_int_float():
    # Test sorting a list with both integers and floats
    codeflash_output = sorter([1, 2.2, 0.5, 3]) # 5.66μs -> 5.23μs (8.28% faster)

# 2. Edge Test Cases

def test_sorter_large_negative_and_positive():
    # Test sorting a list with very large and very small numbers
    arr = [999999999, -999999999, 0, 123456789, -123456789]
    expected = [-999999999, -123456789, 0, 123456789, 999999999]
    codeflash_output = sorter(arr) # 5.93μs -> 5.30μs (11.8% faster)

def test_sorter_min_max_int():
    # Test sorting a list with Python's min and max integer values
    import sys
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    expected = [-sys.maxsize-1, 0, sys.maxsize]
    codeflash_output = sorter(arr) # 4.73μs -> 4.32μs (9.53% faster)

def test_sorter_already_sorted_with_duplicates():
    # Test sorting an already sorted list with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    expected = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr) # 5.65μs -> 3.55μs (59.2% faster)

def test_sorter_list_with_one_none():
    # Test sorting a list with a None value (should raise TypeError)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr) # 5.63μs -> 5.99μs (6.03% slower)

def test_sorter_list_with_non_numeric():
    # Test sorting a list with a string value (should raise TypeError)
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr) # 5.64μs -> 5.57μs (1.24% faster)

def test_sorter_list_with_bool():
    # Test sorting a list with boolean values (should sort as 0/1)
    arr = [True, False, 1, 0]
    expected = [False, 0, True, 1]  # Python treats False=0, True=1
    codeflash_output = sorter(arr) # 5.39μs -> 4.77μs (12.9% faster)

def test_sorter_stability():
    # Test stability: sorting objects with same key should preserve order
    class Obj:
        def __init__(self, val, label):
            self.val = val
            self.label = label
        def __lt__(self, other):
            return self.val < other.val
        def __gt__(self, other):
            return self.val > other.val
        def __eq__(self, other):
            return self.val == other.val
        def __repr__(self):
            return f"Obj({self.val},'{self.label}')"
    a = Obj(1, 'a')
    b = Obj(1, 'b')
    c = Obj(2, 'c')
    arr = [c, a, b]
    # Bubble sort is stable, so a should come before b
    codeflash_output = sorter(arr); sorted_arr = codeflash_output # 6.10μs -> 5.39μs (13.2% faster)

def test_sorter_mutation():
    # Test that the original list is mutated (in-place sort)
    arr = [2, 1]
    sorter(arr) # 4.04μs -> 3.75μs (7.65% faster)

def test_sorter_empty_list_type():
    # Test that the return type for empty list is still list
    arr = []
    codeflash_output = sorter(arr); result = codeflash_output # 2.67μs -> 2.65μs (0.792% faster)

def test_sorter_large_float_precision():
    # Test sorting floats with very close values
    arr = [1.000001, 1.0000001, 1.00000001]
    expected = [1.00000001, 1.0000001, 1.000001]
    codeflash_output = sorter(arr) # 4.46μs -> 4.42μs (0.860% faster)

# 3. Large Scale Test Cases

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 55.8ms -> 58.5μs (95330% faster)

def test_sorter_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) # 85.4ms -> 53.7ms (58.9% faster)

def test_sorter_large_random_list():
    # Test sorting a large random list
    import random
    arr = list(range(1000))
    random.shuffle(arr)
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 75.5ms -> 44.3ms (70.5% faster)

def test_sorter_large_list_with_duplicates():
    # Test sorting a large list with many duplicate values
    arr = [5] * 500 + [3] * 250 + [7] * 250
    expected = [3] * 250 + [5] * 500 + [7] * 250
    codeflash_output = sorter(arr) # 63.5ms -> 27.5ms (131% faster)

def test_sorter_large_negative_positive_mixed():
    # Test sorting a large list with negative and positive values
    arr = list(range(-500, 500))
    import random
    random.shuffle(arr)
    expected = list(range(-500, 500))
    codeflash_output = sorter(arr) # 75.7ms -> 44.3ms (71.0% faster)

def test_sorter_large_list_mutation():
    # Test that the large list is sorted in-place
    arr = list(range(999, -1, -1))
    sorter(arr) # 85.3ms -> 54.1ms (57.8% faster)

def test_sorter_large_list_type():
    # Test that the return type for large list is still list
    arr = list(range(1000))
    codeflash_output = sorter(arr); result = codeflash_output # 55.6ms -> 59.3μs (93671% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

⏪ Replay Tests and Runtime

To edit these changes git checkout codeflash/optimize-sorter-mf3j84bu and push.