⚡️ Speed up function sorter by 105%

[codeflash-ai]

📄 105% (1.05x) speedup for sorter in app/sort.py

⏱️ Runtime : 840 milliseconds → 409 milliseconds (best of 21 runs)

📝 Explanation and details

The optimized bubble sort implementation achieves a 2x speedup through three key algorithmic improvements:

1. Reduced inner loop bounds: Changed range(len(arr) - 1) to range(n - i - 1). Since each outer pass bubbles the largest remaining element to its final position, the inner loop can skip checking the already-sorted tail. This reduces iterations from ~13M to ~5M in the profiler results.

2. Early termination: Added a swapped flag that breaks the outer loop when no swaps occur in a pass, indicating the array is sorted. This optimization provides massive gains on already-sorted data - test results show 44,162% speedup on large sorted lists (51ms → 115μs) and 42,598% speedup on identical elements.

3. Tuple unpacking for swaps: Replaced the 3-line temporary variable swap with arr[j], arr[j + 1] = arr[j + 1], arr[j], which is more efficient in Python and reduces the swap overhead from ~24% to ~20% of total runtime.

The optimizations are particularly effective for:

• Already sorted data: Extraordinary speedups (19.6% to 45,723% faster)
• Nearly sorted data: Strong improvements (75-84% faster on large random lists)
• Lists with many duplicates: Significant gains (74-84% faster)
• General unsorted data: Consistent 50-105% speedup

The early termination feature makes this bubble sort adaptive to input characteristics while maintaining the same O(n²) worst-case complexity.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 50 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 scale test cases

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

# unit tests

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

def test_sorter_simple_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.98μs -> 6.67μs (19.6% faster)

def test_sorter_simple_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.64μs -> 8.14μs (6.12% faster)

def test_sorter_simple_unsorted():
    # Unsorted list
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.43μs -> 7.41μs (13.7% faster)

def test_sorter_with_duplicates():
    # List with duplicates
    arr = [4, 2, 2, 5, 1, 4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.97μs -> 8.08μs (11.1% faster)

def test_sorter_single_element():
    # List with one element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.15μs -> 6.12μs (0.507% faster)

def test_sorter_two_elements_unsorted():
    # Two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.95μs -> 6.58μs (5.47% faster)

def test_sorter_two_elements_sorted():
    # Two elements, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.54μs -> 6.01μs (8.78% faster)

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

def test_sorter_empty_list():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 5.29μs -> 5.11μs (3.64% faster)

def test_sorter_all_identical():
    # All elements identical
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 7.74μs -> 6.53μs (18.6% faster)

def test_sorter_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -4, -2, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.44μs -> 7.66μs (10.3% faster)

def test_sorter_mixed_sign_numbers():
    # List with positive and negative numbers
    arr = [3, -1, 0, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.64μs -> 8.00μs (7.96% faster)

def test_sorter_large_and_small_numbers():
    # List with very large and very small numbers
    arr = [999999, -999999, 0, 123456, -123456]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.81μs -> 8.95μs (9.58% faster)

def test_sorter_floats_and_integers():
    # List with floats and integers
    arr = [3.2, 1, 4.5, 2.1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.8μs -> 11.0μs (7.36% faster)

def test_sorter_already_sorted_with_duplicates():
    # Already sorted list with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.92μs -> 6.67μs (33.6% faster)

def test_sorter_stability():
    # Stability: equal elements should not be reordered
    arr = [(1, 'a'), (2, 'b'), (2, 'c'), (3, 'd')]
    # Sort by first element only
    arr_to_sort = [x[0] for x in arr]
    codeflash_output = sorter(arr_to_sort.copy()); result = codeflash_output # 7.15μs -> 6.46μs (10.6% faster)

def test_sorter_mutation():
    # Ensure the input list is mutated (since bubble sort is in-place)
    arr = [3, 2, 1]
    sorter(arr) # 7.82μs -> 7.11μs (9.98% faster)

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

def test_sorter_large_random_list():
    # Large random list
    arr = random.sample(range(-1000, 0), 1000)  # 1000 unique ints between -1000 and 999
    arr_copy = arr.copy()
    codeflash_output = sorter(arr_copy); result = codeflash_output # 71.0ms -> 40.4ms (75.5% faster)

def test_sorter_large_sorted_list():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 51.6ms -> 112μs (45723% faster)

def test_sorter_large_reverse_sorted_list():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 77.8ms -> 51.8ms (50.1% faster)

def test_sorter_large_list_with_duplicates():
    # Large list with many duplicates
    arr = [random.choice([0, 1, 2, 3, 4, 5]) for _ in range(1000)]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr_copy); result = codeflash_output # 62.8ms -> 36.0ms (74.3% faster)

def test_sorter_large_list_all_identical():
    # Large list, all identical
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 49.0ms -> 114μs (42598% faster)

def test_sorter_large_list_negative_and_positive():
    # Large list with negative and positive numbers
    arr = [random.randint(-1000, 1000) for _ in range(1000)]
    arr_copy = arr.copy()
    codeflash_output = sorter(arr_copy); result = codeflash_output # 67.8ms -> 42.4ms (59.9% 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 sys  # used for edge case with sys.maxsize

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

# unit tests

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

def test_sorter_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([]) # 5.71μs -> 5.38μs (5.94% faster)

def test_sorter_single_element():
    # Test sorting a list with one element
    codeflash_output = sorter([42]) # 6.25μs -> 6.12μs (2.26% faster)

def test_sorter_sorted_list():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5]) # 7.63μs -> 6.66μs (14.7% faster)

def test_sorter_reverse_sorted_list():
    # Test sorting a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1]) # 8.44μs -> 8.27μs (2.04% faster)

def test_sorter_unsorted_list():
    # Test sorting a typical unsorted list
    codeflash_output = sorter([3, 1, 4, 5, 2]) # 8.25μs -> 7.43μs (11.0% faster)

def test_sorter_with_duplicates():
    # Test sorting a list with duplicate elements
    codeflash_output = sorter([4, 2, 5, 2, 3, 4, 1]) # 9.62μs -> 8.87μs (8.42% faster)

def test_sorter_with_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -2, 0, 2, 1]) # 8.78μs -> 7.58μs (15.9% faster)

def test_sorter_with_mixed_sign_numbers():
    # Test sorting a list with both positive and negative numbers
    codeflash_output = sorter([0, -10, 5, -2, 3, 1]) # 9.14μs -> 8.39μs (8.87% faster)

def test_sorter_with_all_equal_elements():
    # Test sorting a list where all elements are the same
    codeflash_output = sorter([7, 7, 7, 7, 7]) # 7.58μs -> 6.44μs (17.7% faster)

def test_sorter_with_two_elements():
    # Test sorting a list with two elements
    codeflash_output = sorter([2, 1]) # 6.90μs -> 6.84μs (0.936% faster)
    codeflash_output = sorter([1, 2]) # 3.97μs -> 3.87μs (2.56% faster)

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

def test_sorter_with_large_and_small_integers():
    # Test sorting with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize - 1, 0, 999999999, -999999999]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 10.3μs -> 9.63μs (6.71% faster)

def test_sorter_with_floats():
    # Test sorting a list with floating point numbers
    arr = [3.14, 2.71, -1.0, 0.0, 2.71828]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 11.5μs -> 10.5μs (9.46% faster)

def test_sorter_with_integers_and_floats():
    # Test sorting a list with both integers and floats
    arr = [1, 2.5, 0, -3.5, 2]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 11.9μs -> 10.5μs (13.9% faster)

def test_sorter_with_repeated_extremes():
    # Test sorting a list with repeated min and max values
    arr = [100, -100, 100, -100, 0]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 9.77μs -> 8.35μs (17.1% faster)

def test_sorter_stability():
    # Bubble sort is stable; test stability with tuples (value, original_index)
    arr = [(2, 'a'), (1, 'b'), (2, 'c'), (1, 'd')]
    # Custom comparator: sort by first element only
    # Since our sorter does not support key, we simulate stability check by sorting integers with repeated values
    arr_int = [2, 1, 2, 1]
    expected = [1, 1, 2, 2]
    codeflash_output = sorter(arr_int[:]) # 8.13μs -> 7.32μs (11.2% faster)

def test_sorter_large_negative_and_positive():
    # Test with both large negative and positive numbers
    arr = [-99999999, 99999999, 0, -1, 1]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 10.1μs -> 8.54μs (18.1% faster)

def test_sorter_minimal_difference():
    # Test with numbers that are very close to each other
    arr = [1.000001, 1.0000001, 1.0000002]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 10.2μs -> 8.95μs (13.5% faster)

def test_sorter_mutation_of_input():
    # Test that the input list is mutated (since the implementation sorts in-place)
    arr = [3, 2, 1]
    sorter(arr) # 8.04μs -> 7.07μs (13.8% faster)

def test_sorter_non_integer_types_raise():
    # Test that the function raises TypeError when non-numeric types are present
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr) # 5.72μs -> 5.38μs (6.22% faster)

def test_sorter_with_nan():
    # Test sorting with float('nan') present
    arr = [3, float('nan'), 1]
    # The result is implementation-defined, but float('nan') should not compare less or greater than any number
    codeflash_output = sorter(arr[:]); result = codeflash_output # 8.54μs -> 7.25μs (17.8% faster)
    # Check that all non-nan numbers are sorted, and nan is present
    non_nan = [x for x in result if x == x]

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

def test_sorter_large_random_list():
    # Test sorting a large list of random integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 67.7ms -> 40.8ms (65.9% faster)

def test_sorter_large_sorted_list():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    expected = arr[:]
    codeflash_output = sorter(arr[:]) # 51.0ms -> 115μs (44162% faster)

def test_sorter_large_reverse_sorted_list():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 77.8ms -> 51.3ms (51.5% faster)

def test_sorter_large_duplicates():
    # Test sorting a large list with many duplicate elements
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 62.6ms -> 34.1ms (83.8% faster)

def test_sorter_large_negative_numbers():
    # Test sorting a large list of negative numbers
    arr = [random.randint(-10000, -1) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 70.0ms -> 38.0ms (84.3% faster)

def test_sorter_large_floats():
    # Test sorting a large list of floating point numbers
    arr = [random.uniform(-1e6, 1e6) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 66.7ms -> 37.9ms (75.9% faster)

def test_sorter_large_alternating_signs():
    # Test sorting a large list with alternating positive and negative numbers
    arr = [i if i % 2 == 0 else -i for i in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr[:]) # 64.2ms -> 35.6ms (80.4% 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-mfcr7qu7 and push.