⚡️ Speed up function sorter by 165%

[codeflash-ai]

📄 165% (1.65x) speedup for sorter in src/data/sort.py

⏱️ Runtime : 1.19 seconds → 450 milliseconds (best of 18 runs)

📝 Explanation and details

The optimized code implements three key improvements to the bubble sort algorithm:

1. Early termination with swapped flag: Adds a swapped boolean that tracks if any swaps occurred during a pass. If no swaps happen, the list is already sorted and the algorithm exits early. This is especially powerful for already-sorted or nearly-sorted data.

2. Reduced inner loop iterations: Changes the inner loop from range(len(arr) - 1) to range(n - i - 1). After each pass, the largest i elements are guaranteed to be in their final positions, so we can skip checking them.

3. Optimized swapping: Replaces the three-line temporary variable swap with Python's tuple unpacking arr[j], arr[j + 1] = arr[j + 1], arr[j], which is more efficient at the bytecode level.

Why this leads to speedup: The early termination provides massive gains for sorted/nearly-sorted data (40,000%+ faster on large sorted lists), while the reduced loop bounds decrease total comparisons by roughly half. Even for worst-case scenarios like reverse-sorted lists, the optimizations still provide 56-83% speedup due to fewer loop iterations and efficient swapping.

Test case performance: The optimization excels on already-sorted data, identical elements, and partially-sorted lists, while maintaining good performance on random data. Small lists see minimal improvement due to optimization overhead, but large datasets benefit significantly from the algorithmic improvements.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 59 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random  # used for generating large random lists

# imports
import pytest  # used for our unit tests
from data.sort import sorter

# unit tests

# --- 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 # 8.00μs -> 6.72μs (19.0% 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 # 8.37μs -> 7.97μs (5.06% faster)

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted ascending
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.55μs -> 7.71μs (2.04% slower)

def test_sorter_basic_duplicates():
    # List with duplicates should be sorted and keep duplicates
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.48μs -> 7.58μs (1.34% slower)

def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.31μs -> 6.04μs (12.1% slower)

def test_sorter_basic_empty():
    # Empty list should return empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 4.66μs -> 4.79μs (2.73% slower)

def test_sorter_basic_two_elements_sorted():
    # Two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.94μs -> 6.20μs (4.18% slower)

def test_sorter_basic_two_elements_unsorted():
    # Two elements unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 6.46μs (6.53% slower)

# --- Edge Test Cases ---

def test_sorter_edge_all_same():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.94μs -> 6.25μs (11.1% faster)

def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, -5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.12μs -> 7.96μs (2.12% faster)

def test_sorter_edge_mixed_signs():
    # List with both positive and negative numbers
    arr = [3, -1, 0, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.82μs -> 7.67μs (1.96% faster)

def test_sorter_edge_large_numbers():
    # List with very large numbers
    arr = [999999999, 1, 1234567890, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.53μs -> 8.42μs (1.27% faster)

def test_sorter_edge_floats():
    # List with float numbers
    arr = [3.1, 2.4, 5.6, 1.0, 3.1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.78μs -> 9.88μs (1.08% slower)

def test_sorter_edge_mixed_int_float():
    # List with both ints and floats
    arr = [2, 3.5, 1, 4.0, 2.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.78μs -> 9.62μs (1.68% faster)

def test_sorter_edge_large_small_values():
    # List with both very large and very small values
    arr = [1e-10, 1e10, 0, -1e10, -1e-10]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 14.3μs -> 13.9μs (2.86% faster)

def test_sorter_edge_already_sorted_large():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 47.9ms -> 113μs (42133% faster)

def test_sorter_edge_reverse_sorted_large():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 75.9ms -> 45.7ms (66.2% faster)

def test_sorter_edge_with_zeros():
    # List with zeros and other numbers
    arr = [0, 0, 1, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.84μs -> 7.75μs (14.1% faster)

# --- Large Scale Test Cases ---

def test_sorter_large_random():
    # Large random list
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 68.5ms -> 37.3ms (83.3% faster)

def test_sorter_large_many_duplicates():
    # Large list with many duplicates
    arr = [5] * 500 + [3] * 250 + [8] * 250
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 52.3ms -> 23.2ms (126% faster)

def test_sorter_large_mixed_types():
    # Large list with ints and floats
    arr = [float(i) if i % 2 == 0 else i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 70.1ms -> 169μs (41251% faster)

def test_sorter_large_already_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 47.1ms -> 108μs (43170% faster)

def test_sorter_large_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 72.9ms -> 46.6ms (56.5% faster)

def test_sorter_large_all_same():
    # Large list, all elements the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 47.5ms -> 107μs (44207% faster)

def test_sorter_large_negative_numbers():
    # Large list with negative numbers
    arr = [-(i + 1) for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 73.3ms -> 46.1ms (58.9% faster)

def test_sorter_large_with_zeros():
    # Large list with zeros and other numbers
    arr = [0] * 500 + list(range(500))
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 47.8ms -> 103μs (45974% faster)

# --- Determinism Test ---

def test_sorter_determinism():
    # Sorting the same list multiple times should always yield the same result
    arr = [5, 3, 2, 4, 1]
    codeflash_output = sorter(arr.copy()); result1 = codeflash_output # 8.11μs -> 7.76μs (4.50% faster)
    codeflash_output = sorter(arr.copy()); result2 = codeflash_output # 5.47μs -> 5.14μs (6.32% 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 lists
import string  # used for string edge cases

# imports
import pytest  # used for our unit tests
from data.sort import sorter

# unit tests

# ----------------------
# BASIC TEST CASES
# ----------------------

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 5.02μs -> 5.16μs (2.64% slower)

def test_sorter_single_element():
    # Test sorting a list with one element
    codeflash_output = sorter([42]) # 5.74μs -> 6.09μs (5.70% slower)

def test_sorter_two_elements_sorted():
    # Test sorting a list with two sorted elements
    codeflash_output = sorter([1, 2]) # 5.96μs -> 6.00μs (0.716% slower)

def test_sorter_two_elements_unsorted():
    # Test sorting a list with two unsorted elements
    codeflash_output = sorter([2, 1]) # 6.39μs -> 6.87μs (7.02% slower)

def test_sorter_multiple_elements_sorted():
    # Test sorting a list that's already sorted
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 7.09μs -> 6.24μs (13.7% faster)

def test_sorter_multiple_elements_unsorted():
    # Test sorting a list that's in reverse order
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 7.42μs -> 7.75μs (4.23% slower)

def test_sorter_duplicates():
    # Test sorting a list with duplicate values
    codeflash_output = sorter([3, 1, 2, 3, 2, 1]) # 8.00μs -> 7.92μs (0.959% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -2, 0, 2]) # 7.39μs -> 7.28μs (1.53% faster)

def test_sorter_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([5, -3, 0, 2, -1]) # 7.48μs -> 7.57μs (1.15% slower)

def test_sorter_all_equal():
    # Test sorting a list where all elements are equal
    codeflash_output = sorter([7, 7, 7, 7]) # 6.57μs -> 6.15μs (6.76% faster)

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

def test_sorter_strings():
    # Test sorting a list of strings
    codeflash_output = sorter(["banana", "apple", "cherry"]) # 7.51μs -> 7.48μs (0.401% faster)

def test_sorter_strings_with_empty():
    # Test sorting a list of strings with empty string
    codeflash_output = sorter(["", "a", "b"]) # 7.01μs -> 6.98μs (0.415% faster)

def test_sorter_mixed_types_raises():
    # Test sorting a list with mixed types (should raise TypeError)
    with pytest.raises(TypeError):
        sorter([1, "a", 2]) # 5.22μs -> 5.38μs (3.05% slower)

def test_sorter_floats():
    # Test sorting a list of floats
    codeflash_output = sorter([3.14, 2.71, 0.0, -1.0]) # 9.26μs -> 9.72μs (4.72% slower)

def test_sorter_large_negative_and_positive():
    # Test sorting a list with very large and very small numbers
    codeflash_output = sorter([1e10, -1e10, 0, 1e-10, -1e-10]) # 13.3μs -> 13.8μs (3.48% slower)

def test_sorter_unicode_strings():
    # Test sorting a list of unicode strings
    codeflash_output = sorter(["éclair", "apple", "Éclair", "banana"]) # 9.61μs -> 9.75μs (1.45% slower)

def test_sorter_special_characters():
    # Test sorting a list with special characters
    codeflash_output = sorter(["!", "@", "#", "$", "%"]) # 9.35μs -> 8.20μs (14.1% faster)

def test_sorter_large_integers():
    # Test sorting a list with very large integers
    codeflash_output = sorter([999999999999, 1, 1234567890123456789, -999999999999]) # 8.04μs -> 8.76μs (8.16% slower)

def test_sorter_empty_string_list():
    # Test sorting a list of empty strings
    codeflash_output = sorter(["", "", ""]) # 6.29μs -> 6.48μs (2.81% slower)

def test_sorter_list_with_nan_inf():
    # Test sorting a list containing float('inf'), float('-inf'), and float('nan')
    arr = [float('inf'), float('-inf'), float('nan'), 0]
    # Sorting with nan is undefined, but nan should be last in Python's sort
    codeflash_output = sorter(arr); result = codeflash_output # 8.29μs -> 8.20μs (1.18% faster)

def test_sorter_already_sorted_large():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()) # 48.0ms -> 114μs (41769% faster)

def test_sorter_reverse_sorted_large():
    # Test sorting a large reverse sorted list
    arr = list(reversed(range(1000)))
    codeflash_output = sorter(arr.copy()) # 71.5ms -> 46.3ms (54.2% faster)

def test_sorter_random_large():
    # Test sorting a large random list
    arr = random.sample(range(1000), 1000)
    sorted_arr = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 67.4ms -> 38.1ms (76.9% faster)

def test_sorter_stability():
    # Test sorting stability (relative order of equal elements)
    arr = [(2, 'a'), (1, 'b'), (2, 'c'), (1, 'd')]
    # Sort by first element
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 10.4μs (6.51% faster)

def test_sorter_mutation():
    # Test that the function mutates the input list (in-place sort)
    arr = [3, 2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 6.92μs -> 7.26μs (4.70% slower)

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

def test_sorter_large_random_integers():
    # Test sorting a large list of random integers
    arr = [random.randint(-100000, 100000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 65.6ms -> 37.3ms (75.8% faster)

def test_sorter_large_random_floats():
    # Test sorting a large list of random floats
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 63.4ms -> 35.9ms (76.7% faster)

def test_sorter_large_identical_elements():
    # Test sorting a large list where all elements are identical
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()) # 47.5ms -> 106μs (44608% faster)

def test_sorter_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 = sorter(arr.copy()) # 75.4ms -> 42.1ms (79.1% faster)

def test_sorter_large_already_sorted_strings():
    # Test sorting a large list of already sorted strings
    arr = sorted([''.join(random.choices(string.ascii_letters, k=10)) for _ in range(1000)])
    codeflash_output = sorter(arr.copy()) # 59.6ms -> 160μs (36913% faster)

def test_sorter_large_reverse_sorted_strings():
    # Test sorting a large list of reverse sorted strings
    arr = sorted([''.join(random.choices(string.ascii_letters, k=10)) for _ in range(1000)], reverse=True)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()) # 89.7ms -> 50.5ms (77.6% 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-mfcxvice and push.