⚡️ Speed up function sorter by 81%

[codeflash-ai]

📄 81% (0.81x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 3.79 seconds → 2.10 seconds (best of 5 runs)

📝 Explanation and details

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

1. Early termination optimization: Added a swapped flag that tracks whether any swaps occurred in a pass. If no swaps happen, the array is already sorted and the algorithm breaks early. This provides massive speedups for already-sorted or nearly-sorted data.

2. Reduced inner loop iterations: Changed range(len(arr) - 1) to range(n - i - 1). Since after each pass the largest element "bubbles" to its correct position at the end, we don't need to check those positions again, reducing unnecessary comparisons.

3. Tuple swap optimization: Replaced the three-line temporary variable swap with Python's tuple unpacking arr[j], arr[j + 1] = arr[j + 1], arr[j]. This is more efficient as it's handled at the bytecode level.

The optimizations show different benefits across test cases:

• Massive gains (346x-414x faster) on already-sorted arrays due to early termination
• Significant gains (48-128% faster) on random/reverse-sorted data from reduced iterations
• Modest gains (1-18% faster) on small arrays where the overhead reduction matters

The line profiler shows the optimized version performs ~45% fewer loop iterations overall (55M vs 116M in the inner loop), directly translating to the 81% runtime improvement.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 21 Passed
🌀 Generated Regression Tests	✅ 65 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
benchmarks/test_benchmark_bubble_sort.py::test_sort2	7.57ms	4.92ms	53.9%✅
test_bubble_sort.py::test_sort	917ms	630ms	45.6%✅
test_bubble_sort_conditional.py::test_sort	11.6μs	10.7μs	8.56%✅
test_bubble_sort_import.py::test_sort	926ms	631ms	46.8%✅
test_bubble_sort_in_class.py::TestSorter.test_sort_in_pytest_class	926ms	632ms	46.4%✅
test_bubble_sort_parametrized.py::test_sort_parametrized	582ms	272μs	213513%✅
test_bubble_sort_parametrized_loop.py::test_sort_loop_parametrized	134μs	57.1μs	135%✅

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
from code_to_optimize.bubble_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 # 11.0μs -> 10.2μs (6.91% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list should become sorted
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 10.5μs (1.59% faster)

def test_sorter_basic_unsorted():
    # Unsorted list should be sorted
    arr = [3, 1, 4, 2, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 9.00μs (17.6% faster)

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [3, 1, 2, 3, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 9.92μs (10.1% faster)

def test_sorter_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 9.38μs (6.67% faster)

def test_sorter_basic_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 9.67μs (3.87% faster)

def test_sorter_basic_all_equal():
    # All elements are equal
    arr = [7,7,7,7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.3μs -> 9.50μs (8.77% faster)

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

def test_sorter_edge_empty():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.62μs -> 9.12μs (5.48% faster)

def test_sorter_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 10.0μs (6.25% faster)

def test_sorter_edge_mixed_signs():
    # List with both positive and negative numbers
    arr = [0, -1, 1, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.4μs (4.02% faster)

def test_sorter_edge_large_numbers():
    # List with very large and very small numbers
    arr = [999999, -999999, 0, 123456, -123456]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.2μs -> 11.2μs (9.30% faster)

def test_sorter_edge_floats():
    # List with floats
    arr = [3.1, 2.2, 5.5, 1.0, 4.8]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 14.4μs -> 12.2μs (18.1% faster)

def test_sorter_edge_mixed_int_float():
    # List with both ints and floats
    arr = [2, 3.5, 1, 4.0, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.1μs -> 11.0μs (9.85% faster)

def test_sorter_edge_min_max_values():
    # List with min/max integer values
    import sys
    arr = [sys.maxsize, -sys.maxsize-1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 9.62μs (12.6% faster)

def test_sorter_edge_already_sorted_with_duplicates():
    # Already sorted with duplicates
    arr = [1, 2, 2, 3, 4, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.50μs -> 9.79μs (2.98% slower)

def test_sorter_edge_reverse_sorted_with_duplicates():
    # Reverse sorted with duplicates
    arr = [5, 4, 4, 3, 2, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.6μs -> 10.1μs (14.8% faster)

def test_sorter_edge_large_negative_and_positive():
    # Large negative and positive values
    arr = [-1000, 1000, 0, -500, 500]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 10.8μs (5.81% faster)

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

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.2ms -> 58.2μs (34614% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 33.8ms -> 22.8ms (48.0% faster)

def test_sorter_large_random():
    # Large randomly shuffled list
    import random
    arr = list(range(1000))
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.0ms -> 18.3ms (64.4% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.2ms -> 55.4μs (36401% faster)

def test_sorter_large_alternating():
    # Large list alternating between two values
    arr = [1, 2] * 500
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 24.5ms -> 10.7ms (128% faster)

def test_sorter_large_negative_positive():
    # Large list with negative and positive values
    arr = list(range(-500, 500))
    import random
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.7ms -> 18.5ms (66.0% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [float(i) / 10 for i in range(1000)]
    import random
    random.shuffle(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 29.4ms -> 17.5ms (68.1% faster)

# ---- Mutation Testing Specificity ----

@pytest.mark.parametrize("arr,expected", [
    ([2, 1], [1, 2]),  # swap needed
    ([1, 2], [1, 2]),  # no swap needed
    ([2, 2, 1], [1, 2, 2]),  # duplicates, swap
    ([1, 1, 1], [1, 1, 1]),  # all equal
    ([3, 2, 1], [1, 2, 3]),  # full reverse
    ([1, 3, 2], [1, 2, 3]),  # middle swap
    ([0, -1, 1], [-1, 0, 1]),  # negative, zero, positive
])
def test_sorter_mutation_specific(arr, expected):
    # These cases are chosen to catch off-by-one and comparison bugs
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 67.9μs -> 66.5μs (2.13% faster)

# ---- Type Safety ----

def test_sorter_type_error_on_non_numeric():
    # Should raise TypeError if non-comparable types are present
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 43.0μs -> 41.6μs (3.40% faster)

def test_sorter_type_error_on_none():
    # Should raise TypeError if None is present
    arr = [1, None, 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 40.0μs -> 41.2μs (2.93% 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 scale test cases
import string  # used for non-integer edge cases

# imports
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort import sorter

# unit tests

# 1. Basic Test Cases

def test_basic_sorted_list():
    # Already sorted list should remain unchanged
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 10.8μs (5.78% faster)

def test_basic_reverse_sorted_list():
    # Reverse sorted list should be sorted ascending
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 10.7μs (6.26% faster)

def test_basic_unsorted_list():
    # Unsorted list should be sorted ascending
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.79μs -> 10.2μs (3.68% slower)

def test_basic_duplicates():
    # List with duplicates should be sorted correctly
    arr = [2, 3, 2, 1, 4, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 10.5μs (2.78% faster)

def test_basic_single_element():
    # Single element list should remain unchanged
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.79μs -> 9.46μs (7.05% slower)

def test_basic_two_elements():
    # Two element list, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.2μs -> 9.62μs (5.62% faster)

def test_basic_two_elements_sorted():
    # Two element list, already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.17μs -> 9.25μs (0.908% slower)

# 2. Edge Test Cases

def test_edge_empty_list():
    # Empty list should remain unchanged
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.04μs -> 9.00μs (0.467% faster)

def test_edge_all_identical():
    # All elements identical
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.79μs -> 9.88μs (0.841% slower)

def test_edge_negative_numbers():
    # List with negative numbers
    arr = [-3, -1, -2, -5, -4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 10.5μs (1.19% slower)

def test_edge_mixed_sign_numbers():
    # List with both positive and negative numbers
    arr = [-2, 3, 1, -1, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 9.33μs (11.2% faster)

def test_edge_large_and_small_ints():
    # List with very large and very small integers
    arr = [999999999, -999999999, 0, 123456789, -123456789]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.5μs -> 11.3μs (2.21% faster)

def test_edge_floats():
    # List with floats
    arr = [2.2, 3.3, 1.1, 4.4, 5.5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 12.7μs -> 11.7μs (8.93% faster)

def test_edge_mixed_int_float():
    # List with mixed ints and floats
    arr = [2, 3.5, 1, 4.1, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.9μs -> 11.4μs (4.38% faster)

def test_edge_strings():
    # List of strings should sort lexicographically
    arr = ["apple", "banana", "pear", "orange"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 10.5μs (3.96% faster)

def test_edge_mixed_types_should_fail():
    # List with mixed types should raise TypeError
    arr = [1, "two", 3]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 40.2μs -> 41.2μs (2.23% slower)

def test_edge_single_char_strings():
    # List of single character strings
    arr = ["z", "a", "m", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.1μs -> 10.4μs (2.80% slower)

def test_edge_unicode_strings():
    # List of unicode strings
    arr = ["é", "a", "ç", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 10.6μs (4.31% faster)

def test_edge_empty_strings():
    # List with empty strings
    arr = ["", "a", "", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.83μs -> 9.96μs (1.27% slower)

def test_edge_boolean_values():
    # List with boolean values, should sort as False < True
    arr = [True, False, True, False]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 10.2μs (2.86% faster)

def test_edge_none_in_list_should_fail():
    # List with None should raise TypeError when compared
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy()) # 40.1μs -> 41.3μs (2.92% slower)

def test_edge_large_negative_and_positive():
    # List with large negative and positive numbers
    arr = [-1000000, 1000000, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.4μs -> 10.4μs (0.395% faster)

def test_edge_sorted_descending():
    # List sorted descending, should be sorted ascending
    arr = [10, 9, 8, 7, 6, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.3μs -> 10.8μs (5.01% faster)

# 3. Large Scale Test Cases

def test_large_scale_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.1ms -> 59.2μs (33792% faster)

def test_large_scale_reverse_sorted():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 34.2ms -> 22.8ms (50.3% faster)

def test_large_scale_random_ints():
    # Large random list of ints
    arr = random.sample(range(1000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 30.8ms -> 18.9ms (62.8% faster)

def test_large_scale_duplicates():
    # Large list with many duplicates
    arr = [random.randint(0, 10) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 27.9ms -> 16.7ms (67.6% faster)

def test_large_scale_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 # 30.0ms -> 17.2ms (74.5% faster)

def test_large_scale_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 # 31.5ms -> 19.3ms (62.8% faster)

def test_large_scale_all_identical():
    # Large list, all identical
    arr = [7] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 20.0ms -> 55.9μs (35753% faster)

def test_large_scale_boolean():
    # Large list of booleans
    arr = [random.choice([True, False]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 26.8ms -> 12.4ms (116% faster)

def test_large_scale_empty_strings():
    # Large list with some empty strings
    arr = [""] * 500 + ["a"] * 500
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 21.3ms -> 51.4μs (41379% 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-mg1hlst5 and push.

[aseembits93]

diff --git a/code_to_optimize/bubble_sort.py b/code_to_optimize/bubble_sort.py
index 9e97f63a0..76fb52923 100644
--- a/code_to_optimize/bubble_sort.py
+++ b/code_to_optimize/bubble_sort.py
@@ -1,10 +1,13 @@
 def sorter(arr):
     print("codeflash stdout: Sorting list")
-    for i in range(len(arr)):
-        for j in range(len(arr) - 1):
+    n = len(arr)
+    for i in range(n):
+        swapped = False
+        for j in range(n - i - 1):
             if arr[j] > arr[j + 1]:
-                temp = arr[j]
-                arr[j] = arr[j + 1]
-                arr[j + 1] = temp
+                arr[j], arr[j + 1] = arr[j + 1], arr[j]
+                swapped = True
+        if not swapped:
+            break
     print(f"result: {arr}")
     return arr
diff --git a/code_to_optimize/bubble_sort.py b/code_to_optimize/bubble_sort.py
index 9e97f63a0..76fb52923 100644
--- a/code_to_optimize/bubble_sort.py
+++ b/code_to_optimize/bubble_sort.py
@@ -1,10 +1,13 @@
 def sorter(arr):
     print("codeflash stdout: Sorting list")
-    for i in range(len(arr)):
-        for j in range(len(arr) - 1):
+    n = len(arr)
+    for i in range(n):
+        swapped = False
+        for j in range(n - i - 1):
             if arr[j] > arr[j + 1]:
-                temp = arr[j]
-                arr[j] = arr[j + 1]
-                arr[j + 1] = temp
+                arr[j], arr[j + 1] = arr[j + 1], arr[j]
+                swapped = True
+        if not swapped:
+            break
     print(f"result: {arr}")
     return arr