⚡️ Speed up function mysorter by 12%

[codeflash-ai]

📄 12% (0.12x) speedup for mysorter in codeflash/bubble_sort.py

⏱️ Runtime : 2.77 milliseconds → 2.48 milliseconds (best of 107 runs)

📝 Explanation and details

The optimization reduces I/O overhead by consolidating two separate print() calls into a single print statement. Instead of making two system calls to output text, the optimized version combines both messages using a newline character (\n) in a single formatted string.

Key changes:

• Eliminated one print() call by merging the messages: "codeflash stdout: Sorting list" and f"result: {arr}" into one f-string
• Moved the print statement to after sorting to maintain the same output order

Why this creates a speedup:
Each print() call involves system-level I/O operations including buffer flushing and potential context switches. By reducing from two I/O operations to one, the optimization eliminates this overhead. The line profiler confirms this - the original version spent 16.3% + 60.4% = 76.7% of total time on printing, while the optimized version spends only 74.1% on a single print operation.

Performance characteristics:
The optimization shows consistent 60-80% speedup on smaller test cases (basic sorting scenarios) where I/O overhead dominates the runtime. For larger datasets (1000+ elements), the speedup is more modest (2-10%) since the sorting operation becomes the dominant factor. The optimization is particularly effective for small to medium lists where print overhead is significant relative to the actual sorting time.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 3 Passed
🌀 Generated Regression Tests	✅ 55 Passed
⏪ Replay Tests	✅ 1 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_bubble_sort.py::test_sort	273μs	260μs	✅5.02%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large scale/randomized test cases
import string  # used for string sorting test cases
import sys  # used for maxsize/minsize edge cases

# imports
import pytest  # used for our unit tests
from codeflash.bubble_sort import mysorter

# unit tests

# -------------------------
# 1. Basic Test Cases
# -------------------------

def test_empty_list():
    # Test sorting an empty list returns an empty list
    arr = []
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.3μs -> 6.33μs (78.3% faster)

def test_single_element():
    # Test sorting a list with a single element returns the same list
    arr = [42]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.2μs -> 6.29μs (77.5% faster)

def test_sorted_list():
    # Test sorting an already sorted list returns the same list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.2μs -> 6.50μs (71.8% faster)

def test_reverse_sorted_list():
    # Test sorting a reverse-sorted list returns a sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.2μs -> 6.54μs (70.7% faster)

def test_duplicates():
    # Test sorting a list with duplicate elements
    arr = [3, 1, 2, 3, 2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.6μs -> 6.67μs (73.7% faster)

def test_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-1, -3, 2, 0, -2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.5μs -> 6.58μs (74.1% faster)

def test_floats_and_integers():
    # Test sorting a list with both floats and integers
    arr = [1.5, 2, 0.5, 3, 2.5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 12.8μs -> 7.96μs (60.7% faster)

def test_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.5μs -> 6.42μs (79.2% faster)

def test_mixed_case_strings():
    # Test sorting a list of strings with mixed case
    arr = ["Banana", "apple", "Cherry"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.5μs -> 6.54μs (75.8% faster)

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

def test_all_identical_elements():
    # Test sorting a list where all elements are identical
    arr = [7] * 10
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.5μs -> 6.79μs (69.3% faster)

def test_large_and_small_numbers():
    # Test sorting a list with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize, 0, 1, -1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.8μs -> 7.08μs (67.1% faster)

def test_large_and_small_floats():
    # Test sorting a list with very large and very small floats
    arr = [1e308, -1e308, 0.0, 1e-308, -1e-308]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 14.8μs -> 9.79μs (50.6% faster)

def test_nan_and_inf():
    # Test sorting a list with NaN and infinity values
    arr = [float('nan'), float('inf'), -float('inf'), 0.0]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.8μs -> 6.96μs (69.5% faster)

def test_unicode_strings():
    # Test sorting a list of unicode strings
    arr = ["café", "apple", "banana", "ápple"]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 12.0μs -> 7.17μs (67.4% faster)

def test_empty_strings():
    # Test sorting a list with empty strings
    arr = ["", "a", "b", ""]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.4μs -> 6.62μs (72.3% faster)

def test_heterogeneous_types():
    # Test sorting a list with incompatible types should raise TypeError
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 54.5μs -> 49.1μs (10.9% faster)

def test_nested_lists():
    # Test sorting a list of lists (should sort by first element of each sublist)
    arr = [[2, 3], [1, 2], [2, 2]]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 12.0μs -> 7.29μs (64.6% faster)

def test_none_in_list():
    # Test sorting a list with None and numbers should raise TypeError
    arr = [None, 1, 2]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 54.0μs -> 49.0μs (10.2% faster)

def test_custom_objects_without_lt():
    # Test sorting a list of objects without __lt__ should raise TypeError
    class Dummy:
        pass
    arr = [Dummy(), Dummy()]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 54.2μs -> 49.0μs (10.6% faster)

def test_custom_objects_with_lt():
    # Test sorting a list of objects with __lt__ defined
    class Dummy:
        def __init__(self, val):
            self.val = val
        def __lt__(self, other):
            return self.val < other.val
        def __eq__(self, other):
            return self.val == other.val
        def __repr__(self):
            return f"Dummy({self.val})"
    arr = [Dummy(3), Dummy(1), Dummy(2)]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 16.5μs -> 11.4μs (45.1% faster)

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

def test_large_sorted_list():
    # Test sorting a large already sorted list (performance + correctness)
    arr = list(range(1000))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 59.9μs -> 55.0μs (8.86% faster)

def test_large_reverse_sorted_list():
    # Test sorting a large reverse-sorted list (performance + correctness)
    arr = list(range(999, -1, -1))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 59.8μs -> 55.0μs (8.64% faster)

def test_large_random_list():
    # Test sorting a large list of random integers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 123μs -> 119μs (2.88% 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 = mysorter(arr.copy()); result = codeflash_output # 98.5μs -> 94.0μs (4.79% faster)

def test_large_strings():
    # Test sorting a large list of random strings
    arr = [
        ''.join(random.choices(string.ascii_letters, k=10))
        for _ in range(1000)
    ]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 149μs -> 143μs (4.11% faster)

def test_large_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 418μs -> 412μs (1.50% faster)

def test_large_negative_numbers():
    # Test sorting a large list of negative numbers
    arr = [random.randint(-1000000, -1) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 124μs -> 119μs (3.65% faster)

def test_large_identical_elements():
    # Test sorting a large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 57.7μs -> 52.6μs (9.66% faster)

def test_large_alternating_elements():
    # Test sorting a large list with two alternating values
    arr = [0, 1] * 500
    expected = [0] * 500 + [1] * 500
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 78.4μs -> 73.7μs (6.45% 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 codeflash.bubble_sort import mysorter

# unit tests

# --- BASIC TEST CASES ---

def test_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.6μs -> 6.67μs (73.7% faster)

def test_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.5μs -> 6.50μs (76.9% faster)

def test_basic_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 2, 5]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.3μs -> 6.17μs (83.1% faster)

def test_basic_duplicates():
    # List with duplicate values
    arr = [2, 3, 2, 1, 4]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.3μs -> 6.29μs (80.1% faster)

def test_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.5μs -> 6.71μs (70.8% faster)

def test_basic_mixed_numbers():
    # List with both positive and negative numbers
    arr = [3, -1, 0, -2, 2]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.4μs -> 6.62μs (72.3% faster)

def test_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.2μs -> 6.38μs (75.8% faster)

def test_basic_two_elements():
    # List with two elements
    arr = [2, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.1μs -> 6.38μs (74.5% faster)

# --- EDGE TEST CASES ---

def test_edge_empty_list():
    # Empty list
    arr = []
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.0μs -> 6.04μs (82.1% faster)

def test_edge_all_duplicates():
    # All elements are the same
    arr = [7, 7, 7, 7]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.3μs -> 6.46μs (74.9% faster)

def test_edge_large_negative():
    # Large negative values
    arr = [-999999, -1000000, -888888]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.5μs -> 6.67μs (72.5% faster)

def test_edge_large_positive():
    # Large positive values
    arr = [999999, 1000000, 888888]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.2μs -> 6.42μs (75.3% faster)

def test_edge_mixed_types():
    # List with mixed types (should raise TypeError in Python 3)
    arr = [1, "2", 3]
    with pytest.raises(TypeError):
        mysorter(arr.copy()) # 54.2μs -> 49.2μs (10.2% faster)

def test_edge_floats_and_ints():
    # List with both floats and ints
    arr = [3.1, 2, 2.9, 3]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 12.5μs -> 7.62μs (63.4% faster)

def test_edge_inf_nan():
    # List with inf and nan
    import math
    arr = [2, math.inf, 1, math.nan]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.8μs -> 6.92μs (70.5% faster)

def test_edge_large_gap():
    # List with large gap between numbers
    arr = [1, 1000000, -1000000, 500]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.5μs -> 6.54μs (76.4% faster)

def test_edge_list_with_zero():
    # List containing zero
    arr = [0, -1, 1]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.3μs -> 6.54μs (72.6% faster)

def test_edge_sorted_with_duplicates():
    # Sorted list with duplicates
    arr = [1, 2, 2, 3, 3, 3]
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 11.4μs -> 6.54μs (73.9% faster)

# --- LARGE SCALE TEST CASES ---

def test_large_scale_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 59.9μs -> 54.9μs (9.10% faster)

def test_large_scale_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 60.0μs -> 54.4μs (10.2% faster)

def test_large_scale_random():
    # Large random list
    import random
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 122μs -> 114μs (6.77% faster)

def test_large_scale_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [3] * 250 + [7] * 250
    expected = [3] * 250 + [5] * 500 + [7] * 250
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 57.2μs -> 52.3μs (9.40% faster)

def test_large_scale_negative_positive():
    # Large list with both negative and positive values
    arr = list(range(-500, 500))
    random_arr = arr.copy()
    import random
    random.shuffle(random_arr)
    expected = sorted(random_arr)
    codeflash_output = mysorter(random_arr.copy()); result = codeflash_output # 120μs -> 114μs (5.17% faster)

def test_large_scale_all_same():
    # Large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 57.4μs -> 52.6μs (9.19% faster)

def test_large_scale_floats():
    # Large list of floats
    arr = [float(i) / 10 for i in range(1000, 0, -1)]
    expected = sorted(arr)
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 164μs -> 159μs (3.13% faster)

def test_large_scale_performance():
    # Performance check: sorting should not take excessive time
    import time
    arr = list(range(1000, 0, -1))
    start = time.time()
    codeflash_output = mysorter(arr.copy()); result = codeflash_output # 59.3μs -> 54.8μs (8.37% faster)
    duration = time.time() - start
# 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

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_bubble_sort.py::test_sort	273μs	260μs	✅5.02%

To edit these changes git checkout codeflash/optimize-mysorter-me641kv2 and push.