⚡️ Speed up function sorter by 108%

[codeflash-ai]

📄 108% (1.08x) speedup for sorter in src/app/sort.py

⏱️ Runtime : 910 milliseconds → 437 milliseconds (best of 19 runs)

📝 Explanation and details

The optimized code implements three key optimizations to the bubble sort algorithm that significantly improve performance:

1. Reduced Inner Loop Range: Instead of always iterating through len(arr) - 1 elements, the inner loop now uses range(n - 1 - i). This leverages the fact that after each outer loop pass, the largest element "bubbles up" to its correct position at the end, so we don't need to check those already-sorted elements again. This reduces comparisons from ~14M to ~5.4M hits.

2. Early Exit with Swap Detection: A swapped flag tracks whether any swaps occurred during a pass. If no swaps happen, the array is already sorted and the algorithm exits early with break. This is particularly effective for already-sorted or nearly-sorted data.

3. Tuple Swap: Replaced 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 and eliminates one variable assignment per swap.

Performance Impact: The optimizations show dramatic improvements especially for:

• Already sorted arrays: 42,000%+ speedup (49ms → 115μs) due to early exit after first pass
• Large random arrays: 70-80% speedup due to reduced comparisons
• Nearly sorted data: 14-20% speedup from early termination

The 108% overall speedup comes from the combination of fewer loop iterations (reduced from 14M to 5.4M inner loop hits) and intelligent early termination for favorable input patterns.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 58 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
from src.app.sort import sorter

# unit tests

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

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 5.56μs -> 5.31μs (4.77% faster)

def test_sorter_single_element():
    # Test sorting a list with one element
    codeflash_output = sorter([42]) # 6.45μs -> 6.35μs (1.48% faster)

def test_sorter_already_sorted():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 7.71μs -> 6.56μs (17.5% faster)

def test_sorter_reverse_sorted():
    # Test sorting a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 8.52μs -> 8.10μs (5.17% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 1, 5, 9, 2]) # 9.40μs -> 8.63μs (8.90% faster)

def test_sorter_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = sorter([2, 3, 2, 1, 1]) # 8.41μs -> 7.83μs (7.48% faster)

def test_sorter_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -2, 0, 2]) # 8.08μs -> 7.45μs (8.47% 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]) # 8.49μs -> 7.85μs (8.18% faster)

def test_sorter_all_equal():
    # Test sorting a list where all elements are equal
    codeflash_output = sorter([7, 7, 7, 7]) # 6.66μs -> 6.54μs (1.91% faster)

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

def test_sorter_large_negative_and_positive():
    # Test sorting a list with very large and very small numbers
    arr = [999999, -999999, 0, 2147483647, -2147483648]
    expected = [-2147483648, -999999, 0, 999999, 2147483647]
    codeflash_output = sorter(arr) # 10.2μs -> 9.72μs (5.38% faster)

def test_sorter_floats():
    # Test sorting a list of floats
    arr = [3.1, 2.4, -1.2, 0.0, 2.4]
    expected = [-1.2, 0.0, 2.4, 2.4, 3.1]
    codeflash_output = sorter(arr) # 10.7μs -> 10.2μs (5.01% faster)

def test_sorter_ints_and_floats():
    # Test sorting a list of mixed ints and floats
    arr = [2, 3.5, 1, 2.0, -1]
    expected = [-1, 1, 2, 2.0, 3.5]
    codeflash_output = sorter(arr) # 10.7μs -> 10.3μs (4.09% faster)

def test_sorter_min_max():
    # Test sorting a list with only min and max values
    arr = [float('-inf'), float('inf'), 0]
    expected = [float('-inf'), 0, float('inf')]
    codeflash_output = sorter(arr) # 8.35μs -> 8.02μs (4.10% faster)

def test_sorter_with_zeroes():
    # Test sorting a list with multiple zeroes
    arr = [0, 0, 0, 1, -1]
    expected = [-1, 0, 0, 0, 1]
    codeflash_output = sorter(arr) # 8.51μs -> 8.04μs (5.82% faster)

def test_sorter_alternating_pattern():
    # Test sorting a list with an alternating pattern
    arr = [1, -1, 1, -1, 1, -1]
    expected = [-1, -1, -1, 1, 1, 1]
    codeflash_output = sorter(arr) # 8.77μs -> 8.21μs (6.88% faster)

def test_sorter_mutation_safety():
    # Test that the function mutates the input list (since it sorts in-place)
    arr = [3, 2, 1]
    sorter(arr) # 7.18μs -> 7.18μs (0.014% faster)

def test_sorter_large_numbers():
    # Test sorting a list with extremely large numbers
    arr = [10**10, 10**15, -10**15, 0]
    expected = [-10**15, 0, 10**10, 10**15]
    codeflash_output = sorter(arr) # 9.38μs -> 9.00μs (4.27% faster)

def test_sorter_small_numbers():
    # Test sorting a list with extremely small numbers
    arr = [1e-10, 1e-15, -1e-15, 0]
    expected = [-1e-15, 0, 1e-15, 1e-10]
    codeflash_output = sorter(arr) # 15.5μs -> 12.8μs (20.9% faster)

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

def test_sorter_large_list_sorted():
    # Test sorting a large sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 49.8ms -> 115μs (42838% faster)

def test_sorter_large_list_reverse():
    # Test sorting a large reverse sorted list
    arr = list(reversed(range(1000)))
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 79.6ms -> 49.0ms (62.4% faster)

def test_sorter_large_list_random():
    # Test sorting a large randomly ordered list
    import random
    arr = list(range(1000))
    random.shuffle(arr)
    expected = list(range(1000))
    codeflash_output = sorter(arr) # 70.2ms -> 39.8ms (76.4% faster)

def test_sorter_large_list_duplicates():
    # Test sorting a large list with many duplicates
    arr = [5] * 500 + [3] * 250 + [7] * 250
    expected = [3] * 250 + [5] * 500 + [7] * 250
    codeflash_output = sorter(arr) # 57.2ms -> 24.4ms (135% faster)

def test_sorter_large_list_negative_positive():
    # Test sorting a large list with negative and positive numbers
    arr = list(range(-500, 500))
    import random
    random.shuffle(arr)
    expected = list(range(-500, 500))
    codeflash_output = sorter(arr) # 69.5ms -> 40.8ms (70.1% faster)

def test_sorter_large_list_floats():
    # Test sorting a large list of floats
    arr = [float(i) / 10 for i in range(1000)]
    import random
    random.shuffle(arr)
    expected = sorted(arr)
    codeflash_output = sorter(arr) # 67.1ms -> 38.1ms (76.3% faster)

def test_sorter_large_list_alternating():
    # Test sorting a 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) # 65.0ms -> 37.0ms (76.0% faster)

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

def test_sorter_boolean_values():
    # Test sorting a list with boolean values (True/False are treated as 1/0)
    arr = [True, False, True, False]
    expected = [False, False, True, True]
    codeflash_output = sorter(arr) # 8.80μs -> 8.23μs (6.87% faster)

def test_sorter_strings():
    # Test sorting a list of strings (should sort lexicographically)
    arr = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(arr) # 8.04μs -> 8.02μs (0.237% faster)

def test_sorter_mixed_types_raises():
    # Test sorting a list with mixed types (should raise TypeError)
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr) # 5.40μs -> 5.60μs (3.43% slower)
# 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 testing sorting of strings

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

# unit tests

# -----------------
# Basic Test Cases
# -----------------

def test_sorter_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.64μs -> 6.65μs (14.9% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.21μs -> 8.15μs (0.761% faster)

def test_sorter_basic_unsorted():
    # Random unsorted list
    arr = [3, 1, 4, 5, 2]
    expected = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.13μs -> 7.86μs (3.42% faster)

def test_sorter_basic_duplicates():
    # List with duplicates
    arr = [4, 2, 2, 1, 3, 4]
    expected = [1, 2, 2, 3, 4, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.50μs -> 8.21μs (3.57% faster)

def test_sorter_basic_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2]
    expected = [-3, -2, -1, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.96μs -> 7.26μs (9.62% faster)

def test_sorter_basic_single_element():
    # Single element list
    arr = [42]
    expected = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.04μs -> 5.88μs (2.70% faster)

def test_sorter_basic_two_elements():
    # Two element list
    arr = [2, 1]
    expected = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.81μs -> 6.84μs (0.424% slower)

def test_sorter_basic_empty():
    # Empty list
    arr = []
    expected = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.23μs -> 5.37μs (2.75% slower)

def test_sorter_basic_strings():
    # List of strings
    arr = ["banana", "apple", "cherry"]
    expected = ["apple", "banana", "cherry"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.06μs -> 8.08μs (0.272% slower)

def test_sorter_basic_floats():
    # List of floats
    arr = [3.1, 2.4, 5.6, 1.0]
    expected = [1.0, 2.4, 3.1, 5.6]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 9.79μs (3.41% faster)

def test_sorter_basic_mixed_int_float():
    # List of ints and floats
    arr = [3, 2.2, 5, 1.1]
    expected = [1.1, 2.2, 3, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 9.60μs (4.97% faster)

# -----------------
# Edge Test Cases
# -----------------

def test_sorter_edge_all_identical():
    # All identical elements
    arr = [7, 7, 7, 7, 7]
    expected = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.72μs -> 6.82μs (13.2% faster)

def test_sorter_edge_large_negative():
    # Large negative numbers
    arr = [-100000, -99999, -100001]
    expected = [-100001, -100000, -99999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.93μs -> 7.93μs (0.063% slower)

def test_sorter_edge_min_max_int():
    # Min and max int values
    arr = [2147483647, -2147483648, 0]
    expected = [-2147483648, 0, 2147483647]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.08μs -> 7.78μs (3.88% faster)

def test_sorter_edge_min_max_float():
    # Min and max float values
    arr = [float('inf'), float('-inf'), 0.0]
    expected = [float('-inf'), 0.0, float('inf')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.37μs -> 8.21μs (1.88% faster)

def test_sorter_edge_nan():
    # List with NaN (should place NaN at the end, as Python's sort does)
    arr = [3, float('nan'), 2]
    expected = [2, 3, float('nan')]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.88μs -> 7.37μs (6.85% faster)

def test_sorter_edge_strings_with_case():
    # Strings with different cases
    arr = ["Banana", "apple", "Cherry"]
    expected = ["Banana", "Cherry", "apple"]  # Python sorts uppercase before lowercase
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.84μs -> 7.94μs (1.25% slower)

def test_sorter_edge_unicode_strings():
    # Unicode string sorting
    arr = ["éclair", "apple", "Éclair", "banana"]
    expected = ["Éclair", "apple", "banana", "éclair"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 9.97μs (1.02% faster)

def test_sorter_edge_empty_string_elements():
    # List with empty strings
    arr = ["", "a", "b", ""]
    expected = ["", "", "a", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.38μs -> 7.96μs (5.16% faster)

def test_sorter_edge_boolean_values():
    # List with boolean values
    arr = [True, False, True, False]
    expected = [False, False, True, True]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.09μs -> 7.96μs (1.63% faster)

def test_sorter_edge_mixed_types_should_raise():
    # List with mixed types that can't be compared should raise TypeError
    arr = [1, "a", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 5.40μs -> 5.45μs (0.881% slower)

def test_sorter_edge_tuple_elements():
    # List of tuples (should sort by first element)
    arr = [(2, "b"), (1, "a"), (3, "c")]
    expected = [(1, "a"), (2, "b"), (3, "c")]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.30μs -> 9.24μs (0.584% faster)

def test_sorter_edge_custom_objects_should_raise():
    # List of objects without comparison should raise TypeError
    class NoCmp:
        pass
    arr = [NoCmp(), NoCmp()]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 5.26μs -> 5.14μs (2.24% faster)

# -----------------
# Large Scale Test Cases
# -----------------

def test_sorter_large_sorted():
    # Large sorted list
    arr = list(range(1000))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 49.5ms -> 118μs (41612% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 76.9ms -> 48.6ms (58.3% faster)

def test_sorter_large_random():
    # Large random list
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 70.6ms -> 41.7ms (69.6% 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 # 61.1ms -> 33.2ms (83.8% 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 # 78.8ms -> 45.2ms (74.4% faster)

def test_sorter_large_floats():
    # Large list of random floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 64.8ms -> 38.2ms (69.8% faster)

def test_sorter_large_already_sorted_with_duplicates():
    # Large already sorted list with duplicates
    arr = sorted([random.choice(range(10)) for _ in range(1000)])
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 49.5ms -> 109μs (44988% 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-mfcrffqg and push.